La propensión efectual y la propensión causal como factores determinantes de las intenciones emprendedoras: una aplicación en el sector turístico

Doctoranda: Alicia Martín Navarro Director: Dr. Félix Velicia Martín Sevilla, 2023 LA PROPENSIÓN EFECTUAL Y LA PROPENSIÓN CAUSAL COMO FACTORES DETERMINANTES DE LAS INTENCIONES EMPRENDEDORAS: UNA APLICACIÓN EN EL SECTOR TURÍSTICO 2 AGRADECIMIENTOS Seis años después presento mi segunda tesis doctoral. Al igual que en aquel momento, ha sido un camino laborioso pero lleno de satisfacciones, junto a compañeros y amigos que han participado en el desarrollo de los tres artículos y en el arduo camino de su publicación. Son muchas las personas que han colaborado en este recorrido. Profesionalmente agradezco el apoyo de mis compañeros, y en especial a José Aurelio que, desde que comencé mi andadura por la universidad, ha estado a mi lado enseñándome y participando conmigo en todos los proyectos que le he planteado. A Ricardo, por haberme acogido en su universidad y ayudado en todo lo que he necesitado. Pero sobre todo a Félix, mi director de tesis, por saber guiarme y poner a mi disposición los recursos necesarios para el desarrollo de esta investigación. Personalmente, quiero agradecer la enorme paciencia que ha demostrado mi familia por dejarme recorrer este camino que muchas veces no habéis comprendido, pero igualmente habéis apoyado. Pero, sobre todo, dedico este esfuerzo a mi madre que, aunque ya no esté a mi lado, siento todos los días. Porque siempre confiaste en mí y me apoyabas de una forma incondicional. Esta tesis te la ofrezco a ti. 3 RESUMEN En la actualidad, el impulso económico encuentra su motor en el espíritu emprendedor, convirtiéndose en una herramienta esencial para la creación de empleo y la generación de riqueza. En este escenario, el turismo se destaca como una industria con amplias posibilidades para el desarrollo empresarial. Por ello, los países desarrollados reconocen la importancia de fomentar el emprendimiento, especialmente entre los estudiantes universitarios, ya que la educación superior proporciona una plataforma única para cultivar la capacidad de llevar a cabo proyectos empresariales. La teoría effectuation ha analizado el comportamiento emprendedor en términos de lógica causal y lógica efectual. Sin embargo, la literatura aún no ha explorado este comportamiento en individuos que no han iniciado sus propios negocios. Resulta intrigante estudiar la propensión efectual o causal en aquellos individuos que nunca han emprendido. Ante este vacío en la literatura, este trabajo busca analizar el impacto de la propensión efectual y causal en las intenciones emprendedoras, centrándose en una muestra de 242 estudiantes del grado de turismo de las universidades de Cádiz y Sevilla. Los resultados revelaron que ambas variables son determinantes en las intenciones emprendedoras, contribuyendo así con dos nuevos factores a la academia: la propensión causal y la propensión efectual. Además, estos hallazgos tienen implicaciones prácticas al proporcionar a los agentes públicos y privados herramientas para la evaluación previa de emprendedores potenciales y orientarlos hacia una mejor adaptación a las lógicas efectuales y causales en el entorno empresarial. Palabras clave: Sector turístico, estudiantes universitarios, effectuation, propensión efectual, propensión causal, análisis factorial, PLS-SEM 4 ABSTRACT Today, economic impulse finds its driving force in the entrepreneurial spirit, becoming an essential tool for job creation and wealth generation. In this scenario, tourism stands out as an industry with many possibilities for business development. For this reason, developed countries recognise the importance of encouraging entrepreneurship, especially among university students, as higher education provides a unique platform for cultivating the ability to carry out entrepreneurial projects. Effectuation theory has analysed entrepreneurial behaviour in terms of causal logic and effectual logic. However, the literature has not yet explored this behaviour in individuals who have not started their businesses. Studying the effectual or causal propensity in individuals who have never been involved in entrepreneurship is intriguing. Given this gap in the literature, this paper seeks to analyse the impact of effectual and causal propensity on entrepreneurial intentions, focusing on a sample of 242 undergraduate students of tourism from the universities of Cadiz and Seville. The results revealed that both variables are determinants of entrepreneurial intentions, thus contributing two new factors to the academy: causal propensity and effectual propensity. Moreover, these findings have practical implications by providing public and private agents with tools for pre-assessing potential entrepreneurs and guiding them towards better adapting to the effectual and causal logics in the business environment. Keywords: Tourism sector, university students, effectuation, effectual propensity, causal propensity, factor analysis, PLS-SEM. 5 Tabla de contenido CAPÍTULO 1 Introducción y objetivos ................................................................................ 7 Introducción ............................................................................................... 8 Intenciones emprendedoras ...................................................................... 12 Propensión efectual y causal .................................................................... 19 Objetivos de investigación ....................................................................... 22 Estructura de la tesis ................................................................................ 23 CAPÍTULO 2 How effectual will you be? Development and validation of a scale in higher education ..................................................................................................... 25 Referencia bibliográfica ........................................................................... 26 CAPÍTULO 3 Impact of Effectual Propensity on Entrepreneurial Intention ..................... 52 Referencia bibliográfica ........................................................................... 53 CAPÍTULO 4 Causal Propensity as an antecedent of Entrepreneurial Intentions ............. 93 Referencia bibliográfica ........................................................................... 94 CAPÍTULO 5 Resultados y Discusión ............................................................................. 128 Resultados de la validación de la escala de medida ............................... 129 6 Impacto de la Propensión Efectual en las Intenciones Emprendedoras 132 Impacto de la Propensión Causal en las Intenciones Emprendedoras ... 137 CAPÍTULO 6 Conclusions, implications, limitations and future agenda ........................ 142 Conclusions ............................................................................................ 143 Theoretical implications ......................................................................... 145 Practical implications ............................................................................. 146 Social implications ................................................................................. 147 Limitations and future lines of research ................................................. 148 Referencias Bibliográficas ........................................................................ 150 7 CAPÍTULO 1 Introducción y objetivos 8 Introducción En la era contemporánea, el tejido económico global ha experimentado una notable transformación impulsada por el fenómeno del emprendimiento. Este concepto, se posiciona como el motor que impulsa la innovación y el crecimiento en las estructuras económicas. A medida que las economías evolucionan y se adaptan a un entorno que cambia constantemente, comprender el papel que desempeña el emprendimiento se convierte en algo fundamental. De esta manera, la actividad económica está impulsada por el espíritu emprendedor y lo convierte en una herramienta esencial para la creación de empleo y la generación de riqueza (Oosterbeek et al., 2010). En un contexto de transformación en que surge la necesidad de superar numerosos desafíos económicos, ha surgido la convicción de que los autónomos deben asumir un papel más activo. Además, deben mostrarse como agentes capaces de desarrollar una trasnformación. Más allá de la mera respuesta a crisis económicas, el autoempleo se presenta como un camino hacia la innovación, la empleabilidad y el desarrollo económico y social. Por un lado, los autónomos, motivados por encontrar soluciones creativas para mantener sus empresas, se convierten en agentes de innovación. Este impulso innovador se traduce en la mejora de productos y servicios, al igual que de la estructura misma de la actividad empresarial para la adaptación y el progreso (Montañés-Del-Río & Medina- Garrido, 2020). Por otro lado, la flexibilidad del autoempleo se adapta tanto a las necesidades del mercado, como empodera a los autónomos para cultivar sus propias habilidades. En definitiva, el autoempleo aparece como un motor fundamental para el desarrollo económico y social. Igualmente es necesario destacar que la capacidad de los autónomos para generar oportunidades económicas impulsa el crecimiento local, cohesiona a la sociedad al fomentar la participación en la creación de valor. Y, este proceso trasciende la supervivencia económica para convertirse en una fuerza propulsora que fortalece las comunidades y las economías en un mundo en constante cambio (Sánchez García et al., 2017). A medida que la investigación avanza, se consolida una conexión beneficiosa entre el espíritu emprendedor, el crecimiento económico y la mitigación de la pobreza. Los estudios emergentes revelan la complejidad del impacto de esta relación. Los investigadores, al profundizar en este campo, destacan cómo el espíritu emprendedor puede impulsar el crecimiento económico, y participa en la eliminación de las barreras 9 que perpetúan la pobreza. La creatividad y la iniciativa inherentes al espíritu emprendedor generan nuevas oportunidades económicas, a la vez que desafían las estructuras de desigualdad (Sabirov, 2022; Vargas-Hernández et al., 2021). En este sentido, se destaca la importancia del fomento del espíritu emprendedor en todos los países. Al cultivar el emprendimiento, las naciones impulsan la vitalidad de sus economías y desencadenan efectos beneficiosos en los disitintos sectores. La contribución del espíritu emprendedor se manifiesta de diversas maneras, comenzando por su capacidad para aumentar la productividad. Los emprendedores, introducen eficiencias y soluciones creativas que elevan competitividad en los sectores económicos. Este impulso en la productividad crea un terreno adecuado para la generación de empleo y la expansión de oportunidades laborales. Además, el fomento del espíritu emprendedor también revitaliza los mercados mediante el desarrollo continuo de nuevos productos y servicios. Es por ello, que la capacidad de los emprendedores para identificar nichos de mercado, anticiparse a las demandas y materializar ideas innovadoras inyecta dinamismo y diversidad en la oferta económica (Esfandiar et al., 2019). En virtud de todo lo anterior, los países desarrollados reconocen la necesidad de fomentar el emprendimiento dentro de su población. Este reconocimiento se manifiesta de manera destacada en sus esfuerzos para promover el emprendimiento, con un enfoque particular en las generaciones más jóvenes y, de manera específica, entre los estudiantes universitarios. Al dirigirse específicamente a los jóvenes, los países desarrollados buscan sembrar el emprendimiento en etapas tempranas, desarrollando así individuos que valoran la creatividad, la innovación y sean capaces de correr riesgos (Syahmi et al., 2019). El enfoque hacia los estudiantes universitarios se fundamenta en la idea de que la educación superior proporciona una plataforma única para nutrir el pensamiento crítico y la capacidad de llevar a cabo proyectos visionarios. Al integrar el emprendimiento en los programas académicos y proporcionar recursos específicos, estos países no solo preparan a los estudiantes para enfrentar el mercado laboral con una mentalidad empresarial, sino que también establecen un puente entre el conocimiento teórico y su aplicación práctica en el mundo real (Breznitz & Zhang, 2022; Kitagawa et al., 2022). La literatura académica resalta la importancia de la intención emprendedora como el paso inicial hacia el emprendimiento. Este concepto va más allá de ser simplemente un punto de partida; la intención emprendedora actúa como la fuerza motriz que impulsa la 16 positiva o negativa hacia esta perspectiva. La atracción hacia el deseo de establecer una empresa propia juega un papel crucial en la formación de la intención emprendedora (Ajzen, 1991). Las normas subjetivas reflejan la presión que los individuos reciben de su entorno social para realizar determinados comportamientos. En un entorno empresarial, las SN se refieren a la importancia que los individuos conceden a la aprobación de su círculo más cercano (familia, amigos u otras personas de referencia) respecto a su decisión de emprender un negocio (Liñán, 2008). Y, por último, el control del comportamiento percibido alude a la percepción que tiene el individuo sobre la facilidad o dificultad de emprender, así como a su creencia en la capacidad de ejercer control sobre su propio comportamiento emprendedor. Esta dimensión aborda además de la percepción de las barreras y obstáculos, la confianza en la propia habilidad para superarlos. De esta manera, desempeña un papel fundamental en la formación de la intención emprendedora al influir en la percepción de viabilidad y control del individuo sobre la iniciativa empresarial (Ajzen, 2002). Además, haciendo referencia al sector turístico también existen precedentes de investigaciones que han utilizado el modelo TPB para medir las intenciones emprendedoras en dicho sector. En este sentido, Kusumawardani et al. (2020) emplearon el marco conceptual proporcionado por el TPB como herramienta para evaluar las intenciones emprendedoras específicas de las mujeres dentro del sector turístico de Bali. Del mismo modo, Mei et al. (2016) evaluaron la intención emprendedora de los estudiantes de turismo chinos a través del modelo TPB. Y, también, el trabajo de Phuc et al. (2020) investigó los factores que influyen en las intenciones emprendedoras de los estudiantes universitarios de turismo en Vietnam utilizando el modelo TPB. Por otro lado, tal y como se ha hecho alusión más arriba, existen otras muchas variables que pretenden explicar las intenciones emprendedoras. En este sentido, la literatura ha estudiado la intención emprendedora y sus determinantes en profundidad. Entre ellos, encontramos que la proactividad, la creatividad y los programas de aprendizaje que destacan entre los diversos determinantes examinados. La proactividad y la creatividad, en particular, emergen como dos capacidades fundamentales que ejercen una influencia especialmente significativa en la formación de la intención emprendedora. Estos factores se revelan como impulsores clave y se posicionan como elementos que juegan un papel crucial en el proceso de desarrollo de las intenciones emprendedoras. Su impacto resalta la importancia de cultivar la proactividad y la creatividad como estrategias efectivas para fomentar la disposición emprendedora entre los individuos. Es por ello, que se genera la 17 necesidad de programas educativos y de desarrollo que potencien estas capacidades clave en el contexto del emprendimiento (Hansen et al., 2011). Por un lado, los individuos proactivos se distinguen por su disposición a buscar y efectuar cambios, así como a anticiparse a las transformaciones en su entorno antes de que estas ocurran. La proactividad se manifiesta en la toma de la iniciativa, y estas personas no esperan pasivamente a que las circunstancias evolucionen, sino que, más bien, se adelantan a ellas. Su capacidad para provocar cambios se traduce en una influencia activa en su entorno. Este comportamiento proactivo es un elemento clave en el fomento de la intención emprendedora, dado que implica una predisposición a liderar y generar cambios positivos en el entorno empresarial y social (Bateman & Crant, 1993). Por otro lado, la creatividad, entendida como la capacidad de concebir y materializar ideas originales, emerge como un atributo esencial para los emprendedores. Esta habilidad desempeña un papel vital en la identificación de oportunidaes y en la generación de ideas novedosas e innovadoras (Schumpeter, 1934). Las personas creativas tienen más probabilidades de convertirse en empresarios. De esta manera, los empresarios creativos son capaces de visualizar soluciones únicas ante desafíos, concebir productos o servicios innovadores y explorar enfoques fuera de lo convencional. Asimismo, un empresario tiene la capacidad de fomentar la creatividad de sus empleados al otorgarles mayor libertad e independencia para innovar y generar ideas originales. Proporcionar a los empleados esta autonomía no solo potencia la creatividad individual, sino que también contribuye al desarrollo de un entorno laboral dinámico y estimulante. Esta práctica puede impulsar la innovación y la competitividad de la empresa en su conjunto, generando un círculo de creatividad y progreso (Kumar & Shukla, 2022). En el contexto del proceso empresarial, la obtención de conocimientos y los enfoques de aprendizaje desempeñan un papel crucial al influir de manera significativa en la capacidad de identificar y aprovechar nuevas oportunidades (Corbett, 2005). La adquisición constante de conocimientos proporciona a los emprendedores una base sólida que les permite comprender el mercado, evaluar tendencias emergentes y reconocer posibles oportunidades sin explotar (Baron, 2006). Además, los métodos de aprendizaje, ya sean formales o informales, afectan la adaptabilidad y la agilidad del emprendedor para enfrentar los desafíos del entorno empresarial. Así, el compromiso continuo con la adquisición de conocimientos y prácticas de aprendizaje se convierte en un factor clave que potencia la capacidad del empresario para innovar y capitalizar oportunidades 18 emergentes en el dinámico mundo empresarial (Ramos-Rodríguez et al., 2010; Corbett, 2005). De esta manera, la formación en emprendimiento puede potenciar la capacidad de los estudiantes para identificar oportunidades al estimular una mayor atención. Al recibir una formación empresarial, los estudiantes se equipan con las herramientas y el conocimiento necesarios para estar alerta ante nuevas informaciones y tendencias emergentes. Esta sensibilización les permite detectar oportunidades más rápidamente y evaluar mejor su potencial. En esencia, la formación emprendedora contribuye al desarrollo de habilidades técnicas y nutre la mentalidad emprendedora al cultivar la capacidad de estar constantemente en sintonía con el entorno empresarial en evolución (Baron, 2006). De este modo, la formación en emprendimiento es vital, ya que vincula el conocimiento práctico con las habilidades y la confianza necesarias para tener éxito en los negocios (Wilson et al., 2007). La creación e implementación de programas de aprendizaje en emprendimiento pueden resultar costosas. Además, la inversión puede extenderse a la incorporación de expertos especializados, la adquisición de tecnologías educativas avanzadas y la actualización constante de los contenidos para mantener la relevancia y la calidad del programa. Aunque la creación de estos programas puede representar una inversión inicial considerable, los beneficios a largo plazo en términos de fomento del espíritu emprendedor y generación de habilidades empresariales pueden justificar plenamente este gasto. Es por esta razón que, en numerosas ocasiones, se establece una colaboración entre empresas y universidades para cultivar un entorno propicio para el fomento del espíritu emprendedor entre los estudiantes universitarios. Esta asociación busca fusionar la experiencia práctica y los recursos empresariales de las compañías con el entorno académico de las universidades, con el fin de ofrecer programas de aprendizaje en emprendimiento más enriquecedores y alineados con las demandas del mundo empresarial. La sinergia resultante no solo proporciona a los estudiantes acceso a experiencias prácticas del mundo empresarial, sino que también permite a las empresas participar activamente en la formación y desarrollo de futuros emprendedores, contribuyendo así al fortalecimiento de la conexión entre la educación superior y el ámbito empresarial (Nabi & Liñán, 2011). 19 Propensión efectual y causal Durante las últimas décadas, la literatura académica sobre la organización de empresas ha ido cambiando su perspectiva desde el análisis de las funciones organizativas hacia el análisis de los distintos comportamientos organizativos, los valores, culturas y las llamadas orientaciones estratégicas (Werhahn et al., 2015). Según Werhahn et al. (2015), las orientaciones estratégicas, alineadas con los objetivos y metas de una organización, tienen el poder de no solo fomentar, sino también de implantar y modelar determinados comportamientos dentro de la estructura organizativa. Siguiendo a Gatignon & Xuereb (1997), se podría indicar que la orientación estratégica se fundamenta en una filosofía o cultura empresarial que se construye y arraiga en el tejido de las compañías, siendo interiorizado por los empleados. En esencia, la orientación estratégica se convierte en un componente vital que influye en la mentalidad y el comportamiento de los empleados, contribuyendo así a la cohesión y alineación entre la visión estratégica de la empresa y las acciones individuales de los miembros del equipo. Desde una perspectiva más enfocada a la creación de nuevas empresas, el estudio del comportamiento de los emprendedores explicaría cómo desarrollan el proceso emprendedor. De esta manera, se podría comprender cómo crean y hacen crecer nuevas empresas y cómo generan crecimientos económicos (Fisher, 2012). En el estudio del fenómeno emprendedor, destacan las explicaciones que la teoría efectual ofrece sobre el comportamiento del emprendedor (Sarasvathy, 2001). Esta teoría es utilizada cada vez más como una base conceptual para la investigación en el campo del emprendimiento. En la teoría efectual de Sarasvathy (2001), los conceptos de effectuation y causation proveen un marco para estudiar los procesos de emprendimiento en entornos con distintos niveles de incertidumbre. La lógica efectual hace referencia a procesos que se realizan gracias a los posibles resultados que se pueden obtener a partir de los recursos que están al alcance del emprendedor. Sin embargo, el concepto de causation o lógica causal se basa en la predicción. En este caso, emprender implica seleccionar los medios necesarios para crear el resultado deseado. En definitiva, la gran diferencia consiste en que para la lógica causal, las oportunidades son creadas; mientras que para la lógica efectual, las oportunidades son descubiertas (Vaghely & Julien, 2010). Las decisiones relativas a cómo desarrollar una idea de negocio, adquirir los recursos necesarios y aplicar una toma de decisiones eficaz tienen lugar en condiciones de 20 incertidumbre (Villani et al., 2018). Durante mucho tiempo, los investigadores en emprendimiento han asumido que los individuos que persiguen oportunidades empresariales se guían únicamente por un comportamiento racional y orientado a objetivos (Perry et al., 2012). Este pensamiento se encuadra en un modelo de comportamiento causal del emprendedor (Sarasvathy, 2001). En contraposición, la teoría del comportamiento efectual sobre emprendimiento propone un enfoque diferente para explicar cómo toman decisiones algunos emprendedores (Sarasvathy, 2001). Así, causation y effectuation representan dos marcos distintos, aplicables a la creación de nuevas empresas. El primero se caracteriza por una planificación cuidadosa, mientras que el segundo se basa en una estrategia más flexible, adaptativa y experimental (Read & Sarasvathy, 2005). Teniendo en cuenta el marco anterior, la lógica efectual consiste en que los emprendedores utilizan un conjunto de medios que les vienen ya dados y se enfocan en decidir qué pueden crear con ellos (Sarasvathy, 2001). Durante la creación de nuevas empresas, los emprendedores que siguen un enfoque efectual, a medida que toman decisiones y observan los resultados de esas decisiones, utilizan esta nueva información para cambiar el rumbo. De esta manera, los emprendedores que siguen una lógica efectual son menos propensos a tratar de planificar el futuro y prefieren cambiar los objetivos y la visión inicial que tenían para su nueva empresa. Así pues, en lugar de predecir el futuro, es más probable que trabajen con los medios que tienen bajo su control y que hagan los ajustes necesarios (Dew et al., 2009). Para Sarasvathy (2001), el enfoque efectual provee cinco principios a la toma de decisiones (los medios, las asociaciones, las pérdidas asumibles, los imprevistos y el control): (1) comienza con tus medios (habilidades, conocimientos y relaciones sociales del emprendedor), donde en el proceso de creación de una nueva empresa, los objetivos van surgiendo basándose en los medios disponibles del emprendedor; (2) crea asociaciones para buscar juntos oportunidades de negocio, compartir recursos y trabajar juntos, con menor incertidumbre y mayor control del negocio; (3) céntrate en minimizar el riesgo, y preocúpate más por pérdidas asumibles que por los beneficios que se podrían conseguir; (4) no pienses en los impresvistos como obstáculos, sino como nuevas oportunidades a explotar y, (5) controla el futuro, que es incierto pero controlable porque 21 los emprendedores pueden influir en las tendencias, crear nuevos mercados y afrontar nuevos retos. Por otro lado y siguiendo la teoría de Sarasvathy (2001), la lógica causal busca la predicción y el emprendedor pretende alcanzar su objetivo a partir de la planificación. Desde esta perspectiva, el individuo hace uso de la información a su alcance y va comprobando que su estrartegia se cumpla. En el caso en que no sea así, el individuo estudia las posibles causas de las desviaciones que se produzcan en su plan. La lógica causal es valiosa en aquellas situaciones en las que el mercado en el que opera el emprendedor es definible y medible (Gabrielsson & Politis, 2011). De esta manera, la lógica causal define un modelo para la toma de decisiones y la resolución de problemas basado en la lógica de la predicción. Así, el conocimiento de los empresarios de los medios disponibles y el resultado que desean obtener impulsan la selección de recursos. En este sentido, se supone que el mercado existe independientemente de los empresarios y que su principal tarea es captar la mayor cuota posible de ese mercado. Por este motivo, los empresarios intentan alcanzar este objetivo planificando y recopilando la información necesaria para ver cómo se materializan las estrategias según el plan y también identificando las posibles causas de que el resultado marque desviaciones con respecto a dicho plan (Sarasvathy & Dew, 2005; Sarasvathy, 2001). Para comprender el comportamiento de los individuos debemos determinar su orientación hacia los principios de effectuation o causation. La teoría de Sarasvathy (2001) ha sido ampliamente estudiada por la comunidad académica aplicada a emprendedores que comienzan nuevas empresas (Smolka et al., 2018; Matalamäki et al., 2017; Guo, Cai, & Zhang, 2016; Schmidt & Heidenreich, 2014; Brettel, Mauer, Engelen, & Küpper, 2012; Chandler et al., 2011;). Es decir, a partir de los resultados de las actuaciones del emprendedor. Sin embargo, los estudios no profundizan en la propensión del individuo hacia una lógica u otra cuando el emprendedor potencial aún no ha actuado. En este sentido, la propensión efectual sería aquella que manifiesta un individuo hacia una lógica efectual antes de comenzar una empresa.Y del mismo modo, la propensión causal se definiría como la tendencia del individuo a tomar decisiones o resolver problemas siguiendo una lógica causal. 22 Objetivos de investigación En la revisión previa, se ha identificado una brecha sustancial en la literatura relacionada con la falta de investigación centrada en la propensión efectual y causal. Este vacío se manifiesta en la escasez de estudios dedicados a comprender el comportamiento hacia una lógica efectual o causal antes de iniciar un negocio. Hasta el momento, la literatura existente no ha abordado de manera exhaustiva cómo los emprendedores se inclinan hacia estas distintas lógicas antes de emprender. Explorar a fondo la propensión hacia un comportamiento efectual o causal no solo subsanaría este gap en la literatura académica, sino que también añadiría un valor significativo al ámbito de la investigación en emprendimiento. Este enfoque permitirá captar matices esenciales en las intenciones emprendedoras y en la concepción de nuevos negocios, enriqueciendo la comprensión general del proceso emprendedor y proporcionando perspectivas valiosas para investigaciones futuras. Así, el estudio detallado de la propensión a estas lógicas se erige como una contribución distintiva y esencial para avanzar en el entendimiento de la dinámica emprendedora. En relación a este aspecto, es relevante destacar que el único antecedente parcialmente similar encontrado en la literatura es el trabajo de Werhahn et al. (2015). Estos autores investigaron la orientación efectual de directivos y trabajadores en el contexto corporativo. Para ellos, la orientación estratégica de la empresa hacia una lógica efectual o causal propicia una cultura organizativa que incide en la propensión del comportamiento de los trabajadores hacia alguna de estas lógicas. Por otro lado, y dada la destacada importancia del sector turístico en la economía de Andalucía, la exploración de las intenciones emprendedoras entre los estudiantes de turismo adquiere una relevancia estratégica. En este contexto, centrarnos en los estudiantes de turismo de la Universidad de Cádiz y de la Universidad de Sevilla se presenta como un enfoque clave para comprender cómo las aspiraciones emprendedoras de esta población pueden contribuir al dinamismo y la evolución sostenible del sector turístico andaluz. Explorar las intenciones emprendedoras de este tipo de estudiantes es crucial para el desarrollo actual y futuro de la industria y hostelería (Tsai et al., 2016) en Andalucía. Con la finalidad de cubrir el gap existente en la literatura respecto a la comprensión de la propensión efectual y causal, se platean los siguientes objetivos de investigación: 23 OBJETIVO GENERAL - Analizar la propensión efectual y causal y su relación con las intenciones emprendedoras OBJETIVOS ESPECÍFICOS - Elaborar y validar un instrumento de medida de la propensión efectual y causal - Determinar el impacto de la propensión efectual de los estudiantes de turismo en sus intenciones emprendedoras - Determinar el impacto de la propensión causal de los estudiantes de turismo en sus intenciones emprendedoras Estructura de la tesis El trabajo de investigación que constituye esta tesis doctoral se ha desarrollado por compendio de publicaciones regulado por la normativa correspondiente de la Universidad de Sevilla (Resolución General del 17 de febrero del 2020). De esta manera, se aportan tres aportaciones publicadas con posterioridad a la primera matrícula de tutela académica de la tesis doctoral, y en las que la doctoranda aparece como primera autora. Además, el número de firmantes de los tres artículos, en ningún caso supera los cuatro. Este documento consiste en un reagrupamiento de trabajos de investigación publicados por la doctoranda en una memoria que les ha dado unidad y que se estructura como se muestra más adelante. Del mismo modo, con la presente tesis doctoral se pretende obtener el título de “Mención Internacional”. Para ello, la doctoranda ha realizado una estancia de tres meses en el año 2022, en NECEUBI, Universidade da Beira Interior, Covilhã, Portugal. Por dicho motivo, el presente documento recoge el resumen y las conclusiones en inglés, como lengua habitual para la comunicación científica en el campo de Organización de Empresas, distinta a cualquiera de las lenguas oficiales en España. Y, además, esta tesis cumple con el resto de requisitos para dicha mención internacional. La estructura de esta tesis se ha organizado en seis capítulos que albergan los tres artículos que la componen. Se inicia con un capítulo introductorio, donde se establece el marco que delimita el ámbito de la investigación y se marcan los objetivos de investigación. A continuación, se presentan los tres artículos en capítulos separados. Así pues, el capítulo número dos corresponde al artículo denominado “How effectual will you be? 24 Development and validation of a scale in higher education” que fue publicado en The International Journal of Management Education en el año 2021. El capítulo tres alberga un artículo publicado en 2023 en Journal of Business Research, titulado “Impact of effectual propensity on entrepreneurial intention”. Y, el tercer artículo conforma el capítulo cuatro de esta tesis. Se denomina “Causal propensity as an antecedent of entrepreneurial intentions” y está publicado en 2023, en la revista International Entrepreneurship and Management Journal. El capítulo número cinco corresponde a los resultados y discusión. Y, finalmente, el trabajo concluye con un capítulo de conclusiones, contribuciones, limitaciones y futuras líneas de investigación, que resume los hallazgos clave y cierra de manera integral la investigación. Este diseño estructural ha sido concebido para ofrecer una progresión natural a través de los elementos esenciales de la tesis, facilitando la comprensión y la asimilación de la información presentada. 25 CAPÍTULO 2 How effectual will you be? Development and validation of a scale in higher education 32 According to Sarasvathy (2001), entrepreneurs who have an effectual orientation consider affordable losses to be more important than expected returns. This type of entrepreneur determines how much loss can be assumed and focuses on experimenting with as many strategies as possible to achieve success. Affordable loss becomes an important criterion on which entrepreneurs base their decisions. This way, entrepreneurs reject projects that cost more than they can afford to lose in favour of more affordable projects. Therefore, following this approach, each new venture would be seen as a project where the losses are under control. Additional resources would be incorporated only if the results justified it. 2.6. Contingency orientation Companies have to deal every day with unexpected situations that are challenging to predict, particularly when they operate in uncertain environments (Hitt, Keats, & DeMarie, 1998). These surprises, whether positive or negative, give rise to setbacks. Effectual logic embraces these setbacks to pursue new business or market opportunities (Sarasvathy, 2008). The effectual entrepreneur does not see setbacks as obstacles to overcome, but as new resources and opportunities to exploit (Harmeling, 2011). This type of entrepreneur excels at pivoting to take advantage of unanticipated events (Sarasvathy, 2001). The individual must be willing to adapt in order to capitalise on these contingencies when faced with new information, means, or surprises (Read et al., 2009). Thus, a contingency orientation relates to the ability to make fast, creative, proactive, and effective changes. In other words, the effectual entrepreneur tries to take advantage of unforeseen events continuously and as advantageously as possible. The contingency orientation addresses the question of what the entrepreneur will decide to do about the environment and not how the entrepreneur will adapt to that environment (Werhahn et al., 2015). 2.7. Control orientation Like organisations, individuals want to ensure favourable results (Wiltbank, Read, Dew, & Sarasvathy, 2009). The concept of control has particular relevance when the future is uncertain, as it seeks to exert some kind of influence on that future. This control implies believing that actors can proactively create or co-create their environment (Sarasvathy, 2001). From this perspective, the behaviour of companies and markets depends on individuals who do not conceive the future as an inevitable result of economic or 33 technological forces. Thus, effectual entrepreneurs rarely see an opportunity as being out of their control, and what they do is work to discover and create opportunities (Dew, Read, Sarasvathy, & Wiltbank, 2008). Control orientation implies that individuals perceive the environment as endogenous and therefore controllable. In this way, the individual is motivated to influence socio-economic trends and to create new markets. The control-oriented individual can face new challenges successfully, especially in uncertain situations (Werhahn et al., 2015). 3. Effectual propensity The existing literature has widely validated the measurement of effectual versus causal behaviour for the case of the entrepreneur who has already demonstrated such a behaviour (Brettel et al., 2012; Chandler et al., 2011). However, there is a gap in the literature if the aim is to measure the propensity of individuals to develop effectual or causal behaviours before these behaviours effectively take place. Being able to measure this propensity could have important practical implications. It would allow public, economic, and educational decision-makers to guide and refine these behaviours before they manifest. In this way, the potential entrepreneur will have the capacity to choose to develop an effectual or causal behaviour (or take a mixed position between the two behaviours) depending on the environmental circumstances (Futterer, Schmidt, & Heidenreich, 2018). Adequately training potential entrepreneurs will allow them to consciously consider each of the attributes of both orientations as they develop their entrepreneurial behaviour. As noted above, the literature does not provide a validated scale to measure this effectual or causal propensity. In the methodology section, we adapted Werhahn et al.’s (2015) effectual orientation scale, which they validated in a corporate context. It was useful for this paper because it captures how an effectual strategic orientation at the corporate level can influence the effectual propensity of employees. Moreover, we validated a scale to measure the “causal propensity” of individuals that was adapted from the work of Chandler et al. (2011) and Gabrielsson and Politis (2011). 4. Methodology 4.1. Questionnaire The questionnaire is one of the methods most widely used by researchers in the field of entrepreneurship (Brettel et al., 2012; Dutta, Gwebu, & Wang, 2015; Estrada, Cruz, 34 Jover, & Gras, 2018; Guo et al., 2016). We adapted a questionnaire from the work of Werhahn et al. (2015), Chandler et al. (2011), and Gabrielsson and Politis (2011) to measure effectual and causal propensity in individuals who had not yet effectively developed such types of behaviour. On the one hand, the Werhahn et al. (2015) scale provided a measure of potential effectual behaviour adjusted to the original principles conceptualised by Sarasvathy and her colleagues (Dew et al., 2009; Sarasvathy, 2001) On the other hand, the scales of Chandler et al. (2011), and Gabrielsson and Politis (2011) completed the measurement of individuals’ effectual orientation with the measurement of their causal orientation. When the questionnaire used is an adaptation of one developed by other authors, given that the proposed new instrument will not fully reflect the consistency of the original works, it is essential to re-establish its validity (Mendoza & Garza, 2009). After finalising the initial design, a panel of entrepreneurship experts reviewed the questionnaire. Six researchers, six entrepreneurs and six entrepreneurship promoters participated in this panel. We contacted these experts by telephone or videoconference. Subsequently, a group of 34 students who met the expected profile reviewed the questionnaire. As a result, we produced a well-designed questionnaire that ensured that respondents fully understood the questions. Once the refinement process was complete, we structured the definitive questionnaire into two differentiated sections (Lee, Tsao, & Chang, 2015). The first section included five demographic items: gender, age, nationality, employment status, and whether the student came from an entrepreneurial family. The second section included six categories that added up to a total of 25 questions to determine the effectual or causal propensity of the respondent. 4.2. Sample and data collection To validate the new scale of measurement, we selected a sample of university students in their final year of studying for a business administration degree at the University of Cadiz and the University of Seville (Spain). Although they had not yet demonstrated effectual or causal behaviour, they could show a propensity towards the type of behaviour they would embody if they created a business. It was essential to study this propensity before they received specific training in entrepreneurship since this training would condition them. Therefore, the responses to the questionnaire were gathered in the first semester of the academic year before the students took a course on “business 35 creation” in which they would acquire the knowledge and necessary skills to carry out a business plan. Data collection took place in October and November 2018. We created an online questionnaire using Google’s Forms application (Hariguna, Lai, & Chen, 2016; Jiang & Wu, 2016; Lian, 2017), which allows the user to access the survey through a web link. This tool funnels the data directly into a spreadsheet, which facilitates subsequent statistical processing. We obtained a sample of 230 completed questionnaires from a total of 463 students enrolled, obtaining a response rate of 49.67 %. Of those surveyed, 58.3 % were women. Furthermore, 86.1 % of those surveyed were between 18 and 24 years old, 11.7 % were between 25 and 30 years old, and the rest were more than 30 years old. Students from the University of Seville accounted for 58.3 % of the respondents, while the remaining 41.7 % were from the University of Cadiz. Moreover, 67.4 % of those surveyed were pursuing a business administration degree, while 32.6 % were pursuing a double degree in business administration and law. Of those surveyed, 36.1 % had parents who had owned or currently owned a business, 43.5 % had worked or were working as employees (as compared with 48.3 % who had never worked), and the rest either had been or were self-employed or were both self-employed and employed. Table 1 shows the descriptive information on the sample. 4.3. Measures Following Sarasvathy (2001) and Gabrielsson and Politis (2011), we did not consider effectual propensity and causal propensity to be two extremes on the same scale. Instead, we divided the logic of the respondents’ decision-making into two different variables. Thus, the construct for effectual propensity was different and separate from the construct for causal propensity, making it possible to detect any combination of the two approaches. On the one hand, we adapted the items for the construct “effectual propensity” from the work of Werhahn et al. (2015), which contemplates five dimensions: (1) Means Orientation; (2) Partnership Orientation; (3) Affordable Loss Orientation; (4) Contingency Orientation; and (5) Control Orientation. Means Orientation is the tendency of individuals to use the resources they already have and select a possible outcome that can be achieved with those resources. We measured this variable using three items. Partnership Orientation is the propensity of individuals to seek out partners to develop their projects, committing themselves to achieve mutual benefit. Four items 36 = = = ≤ measured this variable. Affordable Loss Orientation implies the tendency of individuals to make decisions by limiting their losses rather than focusing on expected returns. This variable was measured using three items. Contingency Orientation is the tendency of individuals to accept setbacks generated in uncertain environments as opportunities to be exploited. Four items measured this variable. Control Orientation is the tendency of individuals to proactively create their environment, especially in situations of uncertainty, by working to discover and create new opportunities. We measured this variable using four items. On the other hand, the Causal Propensity of individuals is the tendency to make decisions and solve problems based on the selection of the necessary resources according to the available means and the desired outcome. Seven items measured Causal Propensity. Items one, three, and six were adapted from the work of Chandler et al. (2011). Items two, four, five, and seven were adapted from the work of Gabrielsson and Politis (2011). Following Dittrich, Francis, Hatzinger, and Katzenbeisser (2005), the above items were measured on a seven-point Likert scale, with one corresponding to “strongly disagree” and seven to “strongly agree”. Table 2 shows all the items. 5. Results We used SmartPLS software to carry out the analysis of the measurement model. It allows for analysing the relationships between latent variables and their indicators. This analysis shows whether all the indicators represent their corresponding construct or whether some of them need to be removed. SmartPLS evaluates the measurement of variables based on individual reliability, construct reliability, discriminant validity, and convergent validity. This analysis ensures that the indicators are good at representing their corresponding variable. Reliability ensures that the measurement produces consistent results, and validity ensures that the indicators of a construct measure only their construct and not another. First, the individual reliability of each item was analysed by simple correlations of the indicators with their respective construct. According to Carmines and Zeller (1979), loads must be λ ≥ 0.707 to accept an indicator. However, some researchers consider that this rule of thumb (λ > 0.707) should not be so rigid in the initial stages of scale development (Hair Jr, Black, Babin, & Anderson, 2014). Hair, Black, Babin, and Anderson (2014) establish that indicators with loads between 0.4 and 0.7 could be removed from a scale if their suppression leads to an increase in the 37 mean extracted variance (AVE) or composite reliability (CR) above the suggested threshold value (AVE 0.5; CR 0.7). In any case, researchers should eliminate indicators with very low loads (i.e., 0.4) (Hair Jr, Ringle, & Sarstedt, 2011). In this study, there were no indicators with loads below 0.4, although there were indicators with loads between 0.4 and 0.7 (see Table 2) that could be eliminated at a later stage. Second we analysed the reliability of the construct to see how rigorously the indicators measured the same latent variable. For this purpose, the measures corresponding to the composite reliability (Werts, Linn, & Jo¨reskog, 1974) should be greater than 0.8 (Nunnally & Bernstein, 1995). Dijkstra–Henseler’s rho (ρA) is another indicator to determine the reliability of the construct. Its value must be above 0.7 (Dijkstra & Henseler, 2015). In this study, all indicators met this requirement except for the Dijkstra–Henseler indicator in the construct “affordable losses”, since its result was 0.691, which did not exceed the established value of 0.7. However, we did not remove the indicator since the value was very close to 0.7, it met the composite reliability threshold (which is a measure of greater acceptance than the indicator ρA), and the loads of the indicators that make up this variable were quite high (see Table 2). Subsequently, convergent validity was studied to verify that the indicators represent a single underlying construct. The average extracted variance (AVE) was used as a measure of this validity, requiring values greater than 0.5 (Fornell & Larcker, 1981). The latent variable “Causation” explained less than 50 % of the variance of the indicators that comprised it (Table 2). Table 1. Descriptive information on the sample. Sample 230 (Response rate: 49.67 %) Gender Female: 58.3 %; Male 41.7 % Age 18-24: 86.1 % 25-30: 11.7 % 30-: 2.2 % Parents who own/had owned a business 36.1 % Working experience As employees: 43.5 % As self-employed: 8.2% Never worked: 48.3 % lolmnjhguoyt 38 Table 2. Convergent reliability and validity before eliminating indicators Constructs/Indicators Loads Composite Reliability ρA AVE Means 0.868 0.773 0.686 I use my personal knowledge and experience in the best possible way. 0.825 I pursue those initiatives for which I have great motivation and interest. 0.814 I pursue those initiatives for which I personally have the relevant competencies. 0.845 Partnership 0.812 0.708 0.524 When I work with others, I aim to ensure that gains and risks are shared fairly. 0.608 I approach potential partners very early on in order to jointly co-create new things. 0.811 I enter into relationships with partners who are willing to commit (e.g. invest time) from the onset. 0.816 When new actors appear in my environment, I perceive them as potential partners. 0.634 Affordable Loss 0.819 0.691 0.601 I would only invest in a business what I can afford to lose. 0.823 In a business, I would try to limit the potential loss of initiatives to an acceptable degree, although it could be that by investing more, I would finally obtain benefits. 0.726 I would only invest in my business if the loss of the investment would not ruin the company, although it could be that by investing more, I would finally obtain benefits. 0.775 Contingency 0.880 0.826 0.649 I regard surprises to be new opportunities that I could take advantage of. 0.784 I exploit contingencies as effectively as possible. 0.888 When I have new information, I try to take advantage of it. 0.737 I use setbacks as new opportunities to take advantage of. 0.805 Control 0.835 0.744 0.561 I attempt to shape the environment I operate in. 0.810 I attempt to proactively design my environment with others. 0.816 In a business, we must attempt to create with others new needs for the market. 0.603 I attempt to influence trends. 0.748 Causation 0.835 0.786 0.424 39 I usually design a long-term plan to organise myself in my tasks. 0.689 I prefer to have predetermined goals and to strive to achieve the results of these goals. 0.748 I analyse long-run opportunities and select what I think would provide the best returns. 0.732 I try to avoid uncertain situations to the greatest possible extent. 0.488 When I set goals to achieve, I analyse my competitors in depth. 0.624 I usually implement control processes to make sure I meet the objectives. 0.640 I think my relationships with those who can influence my future should be long term and goal oriented. 0.600 Note: ρA: Dijkstra–Henseler; AVE: Average extracted variance. We removed the following two items to solve this problem: “I try to avoid uncertain situations to the greatest possible extent” and “When I set goals to achieve, I analyse my competitors in depth”. As a result, the AVE reached an acceptable score above the recommended 0.5. Finally, we analysed the discriminatory validity before removing the previous items. The discriminatory validity is the extent to which a given construct is different from other constructs. Following Henseler, Ringle, and Sarstedt (2016), there is discriminant validity when the heterotrait-monotrait ratio of correlations (HTMT) has values below 0.85 (Gold, Malhotra, & Segars, 2001). Table 3 shows that all values were below this threshold, so we did not need to remove any other indicators. After the removal of the two items, we recalculated all previous indicators (see Tables 4 and 5). Individual reliability was met as the loads were within the thresholds defined above. The reliability of the construct was also met as the values of the composite reliability exceeded the limit of 0.8 and the ρA results were also higher than 0.7. Concerning convergent validity, we observed that all AVE values exceeded 0.5, so each item adequately represented a single construct. Finally, after the elimination of the two items, it was verified that discriminant validity still existed since the values of the HTMT ratio between the different variables were less than 0.85 (see Table 5). As indicated above, this research aims to develop and validate a measurement instrument 40 that assesses the propensity of individuals towards effectual or causal behaviour when they have not yet created a company but could potentially do so. We designed a 25-item questionnaire, adapted from the work of Werhahn et al. (2015), Chandler et al. (2011), and Gabrielsson and Politis (2011), that measured six variables — five related to the effectual propensity of the individual (means, partnership, affordable losses, contingency, and control) and one related to causal propensity. Table 3. Discriminant validity before eliminating indicators Means Partnership Affordable Loss Contingency Control Causation Means Partnership 0.713 Affordable Loss 0.170 0.308 Contingency 0.540 0.590 0.314 Control 0.445 0.771 0.244 0.660 Causation 0.512 0.667 0.489 0.562 0.668 Table 4. Convergent reliability and validity after eliminating items Constructs/Indicators Loads Composite Reliability ρA AVE Means 0.868 0.773 0.686 I use my personal knowledge and experience in the best possible way. 0.829 I pursue those initiatives for which I have great motivation and interest. 0.812 I pursue those initiatives for which I personally have the relevant competencies. 0.843 Partnership 0.812 0.705 0.524 When I work with others, I aim to ensure that gains and risks are shared fairly. 0.610 I approach potential partners very early on in order to jointly co-create new things. 0.808 I enter into relationships with partners who are willing to 0.812 41 commit (e.g. invest time) from the onset. When new actors appear in my environment, I perceive them as potential partners. 0.641 Affordable Loss 0.819 0.683 0.602 I would only invest in a business what I can afford to lose. 0.814 In a business, I would try to limit the potential loss of initiatives to an acceptable degree, although it could be that by investing more, I would finally obtain benefits. 0.748 I would only invest in my business if the loss of the investment would not ruin the company, although it could be that by investing more, I would finally obtain benefits. 0.764 Contingency 0.880 0.828 0.648 I regard surprises to be new opportunities that I could take advantage of. 0.780 I exploit contingencies as effectively as possible. 0.888 When I have new information, I try to take advantage of it. 0.743 I use setbacks as new opportunities to take advantage of. 0.802 Control 0.835 0.744 0.561 I attempt to shape the environment I operate in. 0.810 I attempt to proactively design my environment with others. 0.816 In a business, we must attempt to create with others new needs for the market. 0.603 I attempt to influence trends. 0.748 Causation 0.834 0.763 0.504 I usually design a long-term plan to organise myself in my tasks. 0.743 I prefer to have predetermined goals and to strive to achieve the results of these goals. 0.785 I analyse long-run opportunities and select what I think would provide the best returns. 0.757 I usually implement control processes to make sure I meet the objectives. 0.637 I think my relationships with those who can influence my future should be long term and goal oriented. 0.612 Note: ρA: Dijkstra–Henseler; AVE: Average extracted variance 48 Guo, R., Cai, L., & Zhang, W. 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Journal of Business Research, 157, 113604. 54 IMPACT OF EFFECTUAL PROPENSITY ON ENTREPRENEURIAL INTENTION Alicia Martín-Navarro a,b, Felix Velicia-Martín a, José Aurelio Medina-Garrido b,*, Pedro R. Palos- Sánchez a a University of Seville, Spain b INDESS, University of Cadiz, Spain * Corresponding author This was the version submitted to the “Journal of Business Research” and accepted for publication. The final published version can be found at: https://doi.org/10.1016/j.jbusres.2022.113604 We acknowledge that Inderscience holds the copyright of the final version of this work. Please, cite this paper in this way: Martín-Navarro, A., Velicia-Martín, F., Medina-Garrido, J. A., & Palos-Sánchez, P. R. (2023). Impact of effectual propensity on entrepreneurial intention. Journal of Business Research, 157(January). Abstract For decades, entrepreneurship has been promoted in academia and the tourism sector and seen as an opportunity for new business ventures. In entrepreneurial behaviour, effectual logic shows how individuals use their resources to create new opportunities. This paper aims to determine effectual propensity as an antecedent of entrepreneurial intentions. For this purpose, and based on the TPB model, we conducted our research with tourism students from Cadiz and Seville (Spain) universities with Smart PLS 3. The results show that effectual propensity influences entrepreneurial intentions and that attitude and perceived behavioural control mediate between subjective norms and intentions. Our research has a great added value since effectual propensity is studied for the first time as an antecedent of intentions in people who have never been entrepreneurs. Keywords: TPB, entrepreneurial intentions, effectual propensity, tourism students, SEM. Introduction Tourism is one of Spain's most important economic sectors due to its contribution to GDP and its constant growth in recent decades (Lechuga Sancho et al., 2020). In 2019, Spain was considered the world's second most visited tourist destination. Mainly for sun and beach tourism, eighty-two million tourists decided to visit the country, generating an 55 economic benefit of more than 92 billion euros. This growing trend halted drastically from March 2020 due to the closure of borders because of the COVID-19 pandemic. This situation meant that tourism was primarily domestic in the summer of 2020 and 2021. However, although this sector has not yet reached pre-pandemic rates, it is now gradually recovering the number of foreign tourists visiting the country (Cava Jimenez et al., 2022). After Catalonia, the Canary Islands, and the Balearic Islands, the fourth Spanish region with the most tourists is Andalusia, in the country's south. In Andalusia, tourism activity represents an economic engine. Tourism contributed approximately 13% of the region's GDP and employed more than 385,000 people in 2019. This data indicates that the Spanish tourism industry, particularly in Andalusia, is a key factor in the economic development of the territory (García-Machado et al., 2020). Several factors justify Spain's success in the tourism industry. Spain is the third destination in the world in terms of UNESCO World Heritage Sites, with 48 sites. It is also the first country in the world in Natural Areas and Spaces declared Biosphere Reserves by UNESCO (49 sites). More than 300 days of sunshine a year and almost 8,000 km of coastline, as well as ski resorts, rural tourism, and wellness activities, offer the visitor numerous possibilities. The high quality and variety of Spanish gastronomy are internationally renowned. It is one of the world's safest countries and has a high-standard health system. In terms of infrastructure, it has a high level of land, rail, and air connectivity. It is also the second country in the world, after Korea, regarding digital infrastructure. All of the above means that the tourism industry in Spain offers excellent opportunities for investment, according to the Spain for Tourism and Leisure Industry report from the Instituto de Comercio Exterior (ICEX) from "Ministerio de Industria, Comercio y Turismo" (www.investinspain.org). Within the Spanish tourism sector, the hospitality sector is highly competitive. These industries contribute to regional development by including a wide range of tourismrelated activities that bring synergistic value to the industry due to the "coopetition" they experience (Fong et al., 2018). In this regard, tourism offers many entrepreneurship and business development opportunities (Montañés-Del-Río & Medina-Garrido, 2020; Ramos-Rodríguez et al., 2012). This industry promotes economic development in its geographical area of influence, by acting as an engine for other economic sectors. In this context, the tourism entrepreneur is key to developing attractive destinations, creating 56 employment, and increasing labour productivity and income of the local population (Tleuberdinova et al., 2021). Increasingly, researchers recognise the positive relationship between entrepreneurship, economic growth and poverty reduction (Gielnik, M. M., & Frese, 2013; Lateh et al., 2017; Sabirov, 2022; Vargas-Hernández et al., 2021). In this regard, fostering entrepreneurship is crucial in all countries, since it contributes to the development of the economy by increasing productivity, creating new employment opportunities, and revitalising markets through the development of new products (Esfandiar et al., 2019). For this reason, developed countries promote entrepreneurship among their population, especially among the younger generations (Ahmad et al., 2019) and especially among university students (Breznitz & Zhang, 2022; Kitagawa et al., 2022). The academic literature highlights the importance of entrepreneurial intention as a precursor to entrepreneurial decision-making (Maheshwari, 2021). In the initial stages of the entrepreneurial process, the decision to start up a new enterprise is preceded by the detection of opportunities (Ramos-Rodríguez et al., 2010) and the intention of creating a new venture (Liñán, 2008). The Theory of Planned Behaviour (TPB) (Ajzen et al., 1991) proposes a helpful model for the analysis of entrepreneurial intention (Krueger et al., 2000; Liñan, 2004; Soomro et al., 2018; Urban & Ratsimanetrimanana, 2019). By applying TPB concepts, entrepreneurial intention depends on the personal attitude (PA), subjective norms (SN) and perceived behavioural control (PBC) of the potential entrepreneur. In another respect, and according to Sarasvathy (2001), entrepreneurs may apply a causal or effectual logic while creating their new business. To date, scholars have analysed effectual logic versus causal logic by studying the behaviour of individuals who have already created companies (Brettel et al., 2012; Guo et al., 2016; Schmidt & Heidenreich, 2014). However, effectual research fails to analyse entrepreneurial intention (Shahidi, 2020). The literature has not studied the individual propensity towards one or the other logic in the case of the potential entrepreneur who intends to become an entrepreneur for the first time. The theories of entrepreneurship concerning the study of entrepreneurial intention seem incompatible with the theories on the type of entrepreneurial behaviour to develop since they apply to two different moments of the entrepreneurial process. In this respect, there is a gap in the academic literature since 57 effectual logic as an antecedent of entrepreneurial intentions has never been investigated. Considering this gap, a study into the propensity for effectual behaviour before the firm's creation would add value to entrepreneurship research (Martín-Navarro et al., 2021). Based on the above, this paper aims to test the direct and indirect impact of the effectual propensity of potential entrepreneurs on their entrepreneurial intention. The literature on entrepreneurship widely recognises the university's role as a pool of potential entrepreneurs (Lechuga Sancho et al., 2021). The reasons why students do or do not intend to run their own businesses have been the subject of interest in the entrepreneurship literature (Trang & Doanh, 2019). Since the tourism sector offers many opportunities for entrepreneurial development, university students of tourism constitute human capital with great potential for entrepreneurship in this sector. Promoting the entrepreneurial intention of these students will positively impact their entrepreneurial behaviour and, therefore, create jobs and boost the economy (Zhang et al., 2020). For this reason, our study was conducted with the University of Cadiz and the University of Seville tourism students. Exploring the entrepreneurial intentions of this type of student is crucial for the current and future development of the tourism and hospitality industry (Tsai et al., 2016) in Andalusia. In order to achieve our research objective, we developed several research hypotheses based on the model of intentions from the work of Ajzen et al. (1991) and the effectuation theory of Sarasvathy (2001b). In the methodology section, a questionnaire is presented that was designed to collect data to validate our research model through PLS-SEM. The results showed, among other relationships, the positive effect of effectuation propensity on the entrepreneurial intentions of the sample. In this respect, this research fills the gap in the aforementioned literature, thereby adding value to the literature on entrepreneurial intentions. Background Entrepreneurial intention has become a fascinating field in entrepreneurship research. It presents a way to discover people's desire and commitment to create new ventures. Entrepreneurship research shows those personal and contextual factors that motivate the individual to start an entrepreneurial project (Ramos-Rodríguez et al., 2015; Shahzad et al., 2021). 64 Methodology Data collection The tourism degree students, who voluntarily participated in the research, were interviewed using a self-administered questionnaire using non-probabilistic sampling. Experts and researchers reviewed the questionnaire for content validity and included a pilot test on 30 students. After data collection, we obtained a sample of 464 correctly completed questionnaires. Of the sample, 56.71% are students from the University of Seville, while 43.29% are from Cadiz. Concerning the sample, 71.02% of the respondents are women compared to 28.98% of men. Between 18 and 21 years old are 56.04% of the cases, 37.58% are between 22 and 25 years old, and the rest are older than 25; most are Spanish, only 2.6% are foreigners; 31.82% of those surveyed belong to a family whose parents have had or currently have their own business. Moreover, 46.84% of these students have never worked. The rest have worked, are currently working, are self-employed, or work both for themselves and others. Effectual propensity Subjetive norms Attitude Perceived behavioural control Entrepreneurial intentions H1 H2 H3 H4 H5 H6 H7 H8 H9 65 Measurement scales Five latent variables make up our research model. The questionnaire items were formulated using a 7-point Likert scale, where 1 means strongly disagree, and a score of 7 means strongly agree. According to Sarasvathy (2001a) and Gabrielsson and Politis (2011), effectual orientation is measured independently of causal orientation. This research focuses on the EP construct's effectual orientation (see Table 1). Effectual propensity is the tendency of a potential entrepreneur towards effectual logic. Therefore, we determined the scale with the indicators or items necessary to measure this variable, which is not directly observable by the researcher. The indicators of the EP construct were adapted from the work of Martín-Navarro et al. (2021). Effectual propensity is a multidimensional construct composed of five subdimensions: (1) means orientation (measured through three items), relating to the means available to individuals, such as their knowledge, social networks, experience and skills for their new project; (2) association orientation (four items), which considers that entrepreneurs seek partners to start the business and thus achieve mutual benefits; (3) affordable loss orientation (three items), which limits the losses that entrepreneurs can afford beyond which they would stop the investment and would sacrifice expected profits; (4) contingency orientation (four items), where individuals consider setbacks as new opportunities to exploit and; (5) control orientation (four items), which measures the degree to which individuals believe that they can control and influence the environment (see Table 1). In order to measure the constructs of the Theory of Planned Behaviour (PA, PBC, SN, and EI), we employed an adapted version of the Entrepreneurial Intention Questionnaire (EIQ) (Liñán et al., 2011). This scale measures the four variables belonging to the TPB: (1) PA (five items), which is the favourable or unfavourable predisposition towards entrepreneurship (Fishbein & Ajzen, 1977); (2) PBC (five items) refers to the individual's perception of how easy or difficult it is to develop entrepreneurial behaviour (Liñán & Chen, 2006); (3) SN (four items), which indicates the pressure that individuals are under from their immediate circle (family, friends or significant others) when deciding to undertake a project (Ajzen, 1991) and; (4) EI (six items), regarding the certainty of individuals that they will set up a business in the future (Thompson, 2009). The items 66 were randomly mixed and ordered in the questionnaire to avoid the problems of response set bias and the halo effect. Data analysis Partial Least Squares (PLS) is an approach to Structural Equation Models (SEM) (Chin, 1998; Chin & Newsted, 1999; Chin & Todd, 1995). This technique, which combines factor analysis and linear regressions, allows theoretical relationships between latent variables to be analysed through observed data (Hair Jr, Sarstedt, et al., 2014). PLS-SEM is a variance-based approach. Compared to other covariance-based models (CBM), such as Lisrel and Amos, PLS is a powerful analytical method (Lohmöller, 1989). Its advantages over CBM include fewer assumptions for the measurement scales, sample size, and data distribution (Chin, 1998). PLS-SEM estimates complex models with many constructs, indicator variables, and structural trajectories without imposing distributional assumptions on the data (Roldán & Sánchez-Franco, 2012). PLS is a recommended method for studying latent construct models made up of composite (Rigdon, 2016). Another reason for using PLS involves component scores in a subsequent analysis for modelling multidimensional constructs by applying the embedded two-stage approach (Sarstedt et al., 2019). For the model analysis, we have used the SmartPLS 3.2.7 (Ringle et al., 2015) software, which allows us to analyse the relationships between latent variables and their indicators, that is, how these constructs measure their respective indicators. A PLS model is defined by two steps: (1) a measurement model connecting the observable variables or items to their own latent variables; and (2) a structural model that determines the relationships between the latent variables (Ramírez-Correa et al., 2019). RESULTS Analysis of the measurement model In this first step, we examine reliability and validity. Reliability ensures that the measurement produces consistent results, and validity ensures that the indicators of a 67 construct measure the construct they are intended to measure and not another construct. For the treatment of the multidimensional EP construct, we employed the embedded twostage approach (Sarstedt et al., 2019). In PLS, the individual item reliability is assessed by examining the loadings with their respective construct. The reliability of individual items demands that the loadings are higher than 0.707 (Carmines & Zeller, 1979). Items that fail to meet this criterion may be removed. Table 1 shows that only the means orientation (MO) in the EP has a lower loading and must therefore be eliminated from the analysis. The reliability of a construct enables the internal consistency of the indicators in measuring the concept to be tested. Subsequently, the reliability of the construct is analysed through the Dijkstra-Henseler rho_A in all cases with values higher than 0.7 (Dijkstra & Henseler, 2015). Another test to be carried out is that of internal consistency. In this regard, the composite reliability (Werts et al., 1974) should be higher than 0.8, as suggested by Nunnally and Bernstein (1995). In order to complete this analysis, convergent validity is verified. Convergent validity tests whether various items measuring a construct measure the same thing (Henseler et al., 2009). If so, the fit of the items is significant, and they are therefore highly correlated. To assess convergent validity, we use the average variance extracted (AVE) (Fornell & Larcker, 1981). In this case, the values must be greater than 0.5 (Fornell & Larcker, 1981), which holds true, as shown in Table 1. Table 1. Reliability and convergent validity Construct/ Dimension/Indicator Loads rho_A Composite Reliability AVE Effectual Propensity (EP)* 0.766 0.831 0.554 Means orientation 0.642 I use my personal knowledge and experience in the best possible way 0.808 I pursue those initiatives for which I have great motivation and interest 0.872 I pursue those initiatives for which I personally have the relevant competencies 0.828 Partnership orientation 0.742 I approach potential partners very early on in order to jointly co-create new things 0.824 68 I enter into relationships with partners who are willing to commit (e.g., invest time) from the onset 0.617 When new actors appear in my environment, I perceive them as potential partners 0.783 Control orientation 0.760 I attempt to shape the environment I operate in 0.744 I attempt to proactively design my environment with others 0.797 In a business, we must attempt to create with others new needs for the market 0.526 I attempt to influence trends 0.736 Contingency orientation 0.821 I regard surprises to be new opportunities that I could take advantage of 0.844 I exploit contingencies as effectively as possible 0.872 When I have new information, I try to take advantage of it 0.645 I use setbacks as new opportunities to take advantage of 0.840 Attitude (AT) 0.922 0.938 0.751 Being an entrepreneur implies more advantages than disadvantages to me 0.815 A career as entrepreneur is attractive for me 0.906 If I had the opportunity and resources, I’d like to start a firm 0.856 Being an entrepreneur would entail great satisfactions for me 0.863 Among various options, I would rather be an entrepreneur 0.891 Perceived Behavioral Control (PBC) 0.912 0.935 0.741 To start a firm and keep it working would be easy for me 0.820 I am prepared to start a viable firm 0.897 I can control the creation process of a new firm 0.895 I know how to develop an entrepreneurial Project 0.861 If I tried to start a firm, I would have a high probability of succeeding 0.828 Subjective norms (SN) 0.872 0.921 0.796 If you decided to create a company, would your close family approve of that decision? 0.879 If you decided to create a company, would your friends approve of that decision? 0.918 If you decided to create a company, would your colleagues approve of that decision? 0.879 Entrepreneurial intentions (EI) 0.958 0.966 0.827 I am ready to do anything to be an entrepreneur 0.851 My professional goal is to become an entrepreneur 0.931 I will make every effort to start and run my own firm 0.918 I am determined to create a firm in the future 0.936 I have very seriously thought of starting a firm 0.903 I have the strong intention to start a firm someday 0.913 Note: * Multidimensional construct. 69 Discriminant validity indicates the extent to which a construct is different from others. For this analysis, as suggested by Henseler et al. (Henseler et al., 2015), we use heterotrait-monotrait (HTMT) relationships. This relationship is based on the assumption that, for discriminant validity to exist, HTMT correlations (correlations between indicators measuring the same construct) must be higher than heterotrait-heteromethod correlations (correlations between indicators measuring different constructs) (Hair et al., 2019). Table 2 shows the discriminant validity analysis using the heterotrait-monotrait (HTMT) relationship (Henseler et al., 2016). This ratio must have values below 0.85 (Gold et al., 2001) to ensure that discriminant validity is satisfied, as can be verified. Table 2. Discriminant validity (HTMT) EN PC EO EN SN EN PBC 0.560 EP 0.448 0.407 EI 0.805 0.641 0.441 SN 0.415 0.332 0.444 0.370 In the multidimensional construct, the EP, in the first step, eliminated the dimension "affordable loss orientation" because the loads are lower than 0.707. In this second step, the dimension "means orientation" is eliminated for the same reason. Once the measurement model has been refined, and it has been verified that the measurements are reliable and valid, we continue with the second step, the analysis of the theoretical structural model proposed. Structural model After analysing the reliability and validity, the structural model should be evaluated to identify the relationships between the constructs of the proposed research model (Velicia Martín et al., 2020). The structural model or internal model serves to analyse the relationships of dependence between the independent (exogenous) variables and the dependent (endogenous) variables (Sanchez Franco et al., 2007). This analysis tests whether the hypotheses and the relationships of the constructs of the theoretical model are supported (Henseler et al., 2015). 70 The evaluation of the structural model is carried out through the following aspects: collinearity (VIF); the significance of path coefficients (β); R2 values (variance explained); and predictive relevance Q2 (Reyes-Menendez et al., 2018). Furthermore, we employed the global fit measure developed by Henseler et al. (2015): the standardised root mean square residual (SRMR). For this purpose, a bootstrapping technique with 5,000 re-samples is applied in order to test the proposed hypotheses. It is a non-parametric re-sampling technique repeated after randomly replacing the original sample to create many samples for the bootstrap test (Hair et al., 2011). Firstly, to check for collinearity problems, we examined the values of VIF of all the predictor constructions. Hair et al. (2019) set the most restrictive criterion: VIF values should be lower than 3. As can be observed in Table 3, all construct values are lower than the reference value. Therefore, our model presents no collinearity problems. Table 3. Collinearity (VIF values) AT PC EP IN SN AT 1.258 1.524 PBC 1.439 EP 1.141 1.24 1.276 IN SN 1.141 1.234 1.244 Secondly, we analysed the path coefficients (β). These indicate the strength of the relationships between the explanatory variables and the variables to be explained (Ramírez-Correa et al., 2019). The results of the path coefficients are shown in Table 4. In this respect, it is revealed that out of the nine relationships proposed in the theoretical model, seven are significant. The hypotheses from H1 to H4 and from H7 to H9 were supported. As shown in Table 4, hypotheses H1, H2, H4, H7, H8, and H9 have the highest p-values (and hence the observed differences are significant at the 0.001 level). The unsupported hypotheses are H5 and H6. Therefore, the relationship between SN and PBC is not significant, nor is that between SN and EI. 71 Table 4. Results of significance tests of the coefficients of the structural model. Hypothesis β (Standard Path Coeff.) T Statistics P Values CI Sig H1: EPAT 0.279 6.489 0.000 (0.198;0.365) Yes *** H2: EPPBC 0.175 3.90 0.000 (0.085;0.262) Yes *** H3: EPIN 0.077 2.407 0.016 (0.015;0.138) Yes ** H4: SNAT 0.277 5.297 0.000 (0.171;0.379) Yes *** H5: SN PBC 0.084 1.861 0.063 (-0.006;0.171) No n.s. H6: SN EI 0.016 0.534 0.593 (-0.043;0.075) No n.s. H7: ATPBC 0.425 10.345 0.000 (0.344;0.504) Yes *** H8: ATEI 0.587 18.552 0.000 (0.523;0.647) Yes *** H9: PBC EI 0.264 7.382 0.000 (0.193;0.333) Yes *** Note: Significant at p***=<0.001, p**<0.05 Thirdly, we use the coefficient of determination R2 to analyse the structural model. R2 values measure the predictive power of structural models. Interpreted as multiple regression results, the R2 indicates the variance explained by the exogenous variables (Ramírez-Correa et al., 2019). R2 values are shown in Table 5. According to Hair et al. (2014), R2 values above 0.67 are considered high, between 0.67 and 0.33 these are considered moderate, between 0.33 and 0.19 weak, and values below 0.19 are unacceptable. In our case, Entrepreneurial Intention presents a moderate R2, while the values for PA and PBC are weak. The results show (see Figure 2) that the model explains 64.0% of the total variance of Entrepreneurial Intentions. Table 5. R2-values R 2 R 2adjusted Personal Attitude 0.206 0.202 Perceived behavioral control 0.310 0.305 Entrepreneurial intention 0.640 0.637 72 Figure 2. Final Structural Model Note: *p<0.05; **p<0.01; ***p<0.001; ns not supported. Finally, the model's predictive power is analysed, which enables the model's generalisability to other populations to be tested. This generalisation is especially interesting in social science research (Hair, 2021). In this way, PLS-SEM explains the analysis of the structural model already developed. Moreover, it also performs model prediction (Joreskog & Wold, 1982). Evaluating the predictive power of a model implies measuring its performance on other samples (Cepeda et al., 2017). We analyse the model's predictive power through the Q2 indicator (Geisser, 1974). We apply a PLSpredict, an out-of-sample approach developed (Shmueli et al., 2016). Following Roldan and Cepeda (2017) with the PLSpredictive technique, we obtained results expressed through the variables RMSE (R Mean Square Error), MAE (Mean Absolute Error), and Q_predict2. For the model to have predictive power, the value of Q2 must be greater than zero. In our case, all the values in the last column are positive (see Table 6). Similarly, we obtained positive values for RMSE and MAE, showing good predictability (Woodside, 2013). Therefore, this research demonstrated the predictive validity of the proposed model, or its out-of-sample predictability, to predict the values of new cases. 73 Table 6. Evaluation of PLS predictions RMSE MAE Q_prediction2 Attitude 0.901 0.710 0.196 Perceived behavioral control 0.922 0.757 0.156 Entrepreneurial intention 0.901 0.749 0.195 The global model fit is performed using the values of the standardised root mean square residual (SRMR). A value of 0 would represent a perfect fit, although values below 0.08 present a good fit (Henseler et al., 2014). In our model, SRMR presents a value of 0.052, lower than the reference value, and hence our model presents a good fit. As for indirect effects, there are some significant ones among the model constructs (see Table 7). In addition to showing a direct and positive relationship with EI, EP shows significant indirect effects through PA and PBC. Despite not showing a significant direct relationship with EI or PBC, the SN variable shows a significant indirect relationship through PA with EI and PBC variables. Table 7. Indirect effects Specific indirect effects Route (β) Statistics T p-value CI EP -> AT -> IN 0.164 6.204 0.000 (0.110;0.216) EP -> PBC -> IN 0.046 3.342 0.001 (0.022;0.077) EP -> AT -> PBC -> IN 0.031 4.506 0.000 (0.019;0.046) EP -> AT -> PBC 0.118 6.051 0.000 (0.081;0.157) AT -> PBC -> IN 0.112 5.817 0.000 (0.077;0.152) SN -> AT -> IN 0.162 4.973 0.000 (0.100;0.228) SN -> PBC -> IN 0.022 1.846 0.065 (0.000;0.048) SN -> AT -> PBC -> IN 0.031 3.759 0.000 (0.017;0.050) SN -> AT -> PBC 0.118 4.475 0.000 (0.071;0.174) Importance-performance matrix analysis (IPMA) is carried out (see Table 8 and Figure 3). The IPMA assists the PLS-SEM results through a four-quadrant diagram depicted in Figure 3. The vertical axis represents attribute performance, ranging from poor performance to good performance. 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Keywords: Tourism, Causal Propensity, PLS-SEM, Higher Education, Entrepreneurship, Entrepreneurial Intention Introduction Tourism is a field with many opportunities for business development. Many governments have recognised the importance of this sector and its contribution to the economy of the nations (Al-Jubari et al., 2019). Tourism is one of the largest industries in many countries, providing economic growth, foreign exchange earnings and jobs (Rauch & Hulsink, 2015). For this reason, governments promote and support entrepreneurship in this sector. Moreover, creating new businesses is an essential source of innovation, competitiveness and wealth for the rest of the business (Al-Jubari et al., 2019). Entrepreneurial intention is a precursor to the creation of a firm (Lechuga Sancho et al., 2020; Ramos-Rodríguez et al., 2019). The academic literature studies entrepreneurial intentions, often among university students (Dewi & Sutisna, 2019; Laguía et al., 2019; Anjum et al., 2018; Hu et al., 2018). Promoting entrepreneurial intention among tourism students can help transform their mental maps and orient them towards entrepreneurial behaviour. Therefore, exploring how to improve entrepreneurial intentions among tourism students is crucial for developing the industry now and in the future (Zhang et al., 2020). When entrepreneurs start and manage their businesses they may develop different entrepreneurial behaviours, as suggested by effectual and causal logic (Sarasvathy, 2001). From a causal logic, decision-making is done through prediction. In this way, the entrepreneur tries to obtain the largest market share by planning strategies and controlling possible deviations from his plan. However, this paper focuses on the entrepreneurial intention of those individuals who have not yet created a company. For this reason, we will use the term causal propensity proposed by Martín-Navarro et al. (2021) to analyse 97 this type of logic. The propensity for causal entrepreneurial behaviour, creativity, proactivity and learning programmes in entrepreneurship can influence the formation of entrepreneurial intention (Zampetakis et al., 2011; Kumar & Shukla, 2022; Jun et al., 2022; Leiva et al., 2021). In this sense, this paper aims to analyse the determinant power of these factors in the formation of entrepreneurial intention in the case of individuals who have not yet created a business. As mentioned, the literature traditionally studies causal entrepreneurial behaviour in individuals who have already created their businesses. This paper fills a gap in the literature and adds value by studying the impact of individuals' causal propensity on their entrepreneurial intentions for the first time. In this context, these individuals have not yet created their businesses. However, the formation of their entrepreneurial intention is vital as it is a precursor of the entrepreneurial behaviour that eventually leads them to create their businesses. To achieve the research objective, our paper is organised as follows. A literature review is conducted to develop our hypotheses in the following section. The methodology section presents a structural equation model application using SmartPLS software on a sample of 464 cases. Next, we explain the results obtained and the discussion. Finally, the conclusions, contributions, limitations and future lines of research are presented. Theoretical framework and hypothesis development Enterprise creation plays a decisive role in the economy, especially in developing countries, increasing the level of employment and social growth (Farrukh et al., 2017). Nevertheless, entrepreneurship does not only have an economic impact; it must also generate social benefits and be sustainable (Bacq & Alt, 2018). Social entrepreneurship generates social value, new employment opportunities, wealth, and quality of life for the local population (Celebi et al., 2020), benefiting the local community and the society in general (Naderi et al., 2019). Within this type of entrepreneurship, non-profit entrepreneurship stands out in particular, with the aim of eradicating social problems such as poverty, lack of education, poor public health, unemployment, and other social needs that are not satisfied by the public and private sectors (Aquino et al., 2018). The social impact of entrepreneurship is vital in less economically developed countries, which continuously face various social problems. Precisely, the potential of the tourism sector 98 to drive economic growth in these countries, makes entrepreneurship in this sector a tool for developing low-income and underserved local communities, and places communities at the centre of tourism development. Often, communities in need own the tourism assets that provide the experiences and sights that tourists demand (Dolezal & Burns, 2015). In this context, developing sustainable tourism destinations is vital and often favours poorer populations, by providing them with economic and social wealth, plus environmental and even regenerative benefits (Aquino et al., 2018). Many researchers have studied entrepreneurial intention as the stage before creating new businesses. The intention is a good indicator of future entrepreneurial behaviour (Anjum et al., 2018). The literature has studied entrepreneurial intention and its determinants in depth. We find proactivity, creativity, program learning, and causal propensity among the multiple determinants analysed. The first two factors, proactivity and creativity, are two of the abilities that have the most significant influence on entrepreneurial intention (Hansen et al., 2011). On the one hand, proactive individuals seek to make changes or anticipate changes in the environment before they occur. Proactive people tend to take the initiative. These individuals bring about significant changes because they can influence the environment (Bateman & Crant, 1993). On the other hand, creativity is the capacity to create, help entrepreneurs identify opportunities, and create new and innovative ideas (Schumpeter, 1934). Creative people are more likely to become entrepreneurs. Moreover, an entrepreneur can stimulate creativity among his or her employees by giving them more freedom and greater independence to innovate and generate original ideas (Kumar & Shukla, 2022). In the entrepreneurial process, knowledge acquisition, as well as learning methods, have an impact on the identification and exploitation of new opportunities (Corbett, 2005). Entrepreneurship training can help students increase their alertness to opportunities by focusing on new trends and information (Baron, 2006). In this way, entrepreneurship training is vital as it links practical knowledge with the skills and confidence needed to succeed in business (Wilson et al., 2007). Setting up program learning in entrepreneurship can be costly. Therefore, in many cases, a link is established between companies and universities to support and create the right environment to grow the entrepreneurial spirit among university students (Nabi & Liñán, 2011). 99 Finally, the concept of causal propensity is based on the Effectuation Theory. This theory proposes that entrepreneurs can make decisions based on an effectual or causal logic. The effectual logic is based on creating opportunities from the individual's available resources. In contrast, the entrepreneur can also use causal logic. Causal logic is based on predicting the future and setting objectives to be achieved by that prediction. To achieve these objectives, entrepreneurs develop a process of planning, executing and controlling the planned activities. From this perspective, individuals use the information available to them and check that their strategy is being fulfilled. If this is not the case, the entrepreneurs study the possible causes of the deviations in their plan and take corrective measures (Sarasvathy, 2001). However, Effectuation Theory involves studying entrepreneurial behaviour when a company has already been created. It is not possible to analyse causal or effectual behaviours before when studying the determinants of entrepreneurial intention because these behaviours have not yet occurred and are not observable. In this sense, and following the work of Martín-Navarro et al. (2021), it would be possible to analyse individuals' propensity towards these behaviours. However, they have not yet developed these behaviours because they have not yet created their companies. For this reason, and in coherence with the objectives of this paper, we will use the term causal propensity. Causal propensity refers to the individual's tendency to make decisions and solve problems following causation logic. As mentioned above, the academic literature has extensively studied entrepreneurial intentions and multiple determinants that influence entrepreneurial intentions. In our research, we propose four determinants as antecedents of entrepreneurial intentions. Among them program learning. The courses and training provided in educational institutions generate knowledge in students. The path commonly used to start a new project is developed from a causal logic. Thus, causal logic predominates as it is taught today in most business schools. According to the results obtained by Arvidsson et al. (2020), the vast majority of entrepreneurs, in their initial stage, tend to use causal logic as they need to set objectives within a framework of action that they have previously defined. As the entrepreneur gains experience, in a gradual process, he or she changes from a causal logic to an effectual one. Thus, from a causation approach, the organisation focuses on the most predictable aspects of an uncertain future and prediction is closely related to knowledge. The goal is clearly defined with a causal logic, and the available means are organised efficiently to reach that goal. In this way, the individual has a high 100 level of technical knowledge that he or she has previously acquired in his or her formative stage (Héraud & Muller, 2016). Therefore, university education in entrepreneurship is linked to the causal propensity of the entrepreneur. All of the above makes it possible to state the following. H1: Program Learning is positively related to Causal Propensity. The learning environment is essential to support creativity. Researchers have found that learning environments in which ideas are valued and mistakes are seen as a fundamental part of the learning process support creativity (Chan & Yuen, 2014). When teaching is in a group, its values influence the behaviours of its members, who influence each other to foster or constrain creativity (Peppler & Solomou, 2011). Individual learning does not foster creativity. However, the outcome is different in learning courses where students interact with the lecturer and with each other. Thus, with the lecturer's presence, a learning model is created in which university students considerably improve their creative ability. Thus, in a program learning model in which the lecturer acts as a facilitator of learning, the students become the main learning actors. Creativity becomes the main output of the learning process if a dynamic and interactive environment is used, together with the knowledge acquired by the students (Hardika et al., 2018). In this sense, Machali et al. (2021) found a strong correlation between entrepreneurship education and students' creativity. Valaei et al. (2017) found that learning was positively related to creativity within the context of companies within a sample of top managers. Thus, appropriate program learning facilitates the creativity of individuals. Entrepreneurship training should not only focus on theoretical or methodological aspects, such as business plans. Increasingly, universities are striving to offer programmes to train future entrepreneurs in personal and managerial skills and in those personal traits that will help them to succeed. Because creativity is crucial in the entrepreneurial process, it is essential to cultivate it as a fundamental personality trait in individuals intending to become entrepreneurs (Hu et al., 2018). Concerning the proactive behaviours of individuals, some researchers have argued that proactivity is influenced by behaviours learned through training initiatives (Fay & Frese, 2001; Kabanoff & Bottger, 1991). Thus, proactivity as a set of action-directed behaviours is influenced by personality traits such as creativity (Van Veldhoven & Dorenbosch, 2008). The relationship between the 101 creativity of individuals and their proactive capacity allows us to indicate. The above arguments establish the following relationships. H2: Program Learning is positively related to creativity. H3: Creativity is positively related to proactivity. As discussed above, proactive entrepreneurs can exhibit two distinct, non-exclusive behaviours. One behaviour is related to deliberately generating environmental changes to generate opportunities that did not previously exist. Nevertheless, also proactive are those entrepreneurs who foresee the changes that the environment has in store for them and are prepared even before this change takes place. These proactive individuals detect good opportunities much earlier than others, develop better strategies (Seibert et al., 2001), and develop better strategic vigilance (Yeşilkaya & Ylldlz, 2022). Strategic monitoring is a tool for predicting the future that involves the ability to acutely evaluate information regarding competitors and customers (Yeşilkaya, 2015). Thanks to this ability, entrepreneurs can anticipate and plan what they will offer to their customers (Mahmoud & Mahdi, 2019), anticipate the steps to take in the face of competition, convert threats in the environment into opportunities and establish strategies to adapt to change (Alshaer, 2020; Yeşilkaya, 2015). Furthermore, establish strategies to adapt to change (Yeşilkaya & Ylldlz, 2022). Therefore, strategic vigilance and the ability to detect opportunities enable proactive individuals to rationally plan to take advantage of these opportunities and set goals to be achieved within a causal logic. Similarly, proactivity will support individuals' causal propensity because the more significant the ability to spot opportunities before others, the easier it is to plan how to take advantage of them. Therefore, as indicated below, individuals' proactivity can be connected to a potential causal behaviour, i.e. causal propensity. H4: Proactivity is positively related to Causal Propensity. The uncertainty that entrepreneurs face when creating new ventures involves factors that cannot be predicted and unknown variables. Causal logic understands that the future can be predicted if sufficient information is obtained, for example, through market research. In the causal process, a lot of time and resources are invested in developing a plan to 102 succeed in an existing market (Sarasvathy, 2008; 2001). When individuals set up a new enterprise, they pursue performance and plan how to reach that goal. From a causal approach, both issues are fundamental to forming entrepreneurial intentions (Dutta et al., 2015). The causation approach assists the entrepreneur in developing a new business (Sarasvathy, 2001b) and gathering resources efficiently, working under the entrepreneur's strategy (Delmar & Shane, 2004). In this regard, Li et al. (2020)found that causal logic positively affects entrepreneurial behaviour in a sample of managers in Pakistan. By analogy, if the individual is not yet entrepreneurial, causal propensity should positively impact entrepreneurial intentions, as hypothesised below. H5: Causal Propensity is positively related to Entrepreneurial Intention. Proactive individuals can propose constructive changes that can lead to more appropriate systems. On the other hand, passive and inactive individuals are left to the circumstances as they occur (Kumar & Shukla, 2022). A proactive personality is a key driver for achieving an advantageous outcome in challenging situations (Gupta & Bhawe, 2007). Proactivity is a crucial element of foresight. It is linked to the management and entrepreneurial behaviour. It allows exploring uncertainty while simultaneously executing actions that can influence the future (Djuricic & Bootz, 2019). It is precisely proactivity that helps the entrepreneur to survive in a turbulent environment full of uncertainties (Godet, 1993). Sidratulmunthah et al. (2018) found empirical evidence of the relationship between proactivity and entrepreneurial intentions in a sample of 306 female students from business universities in Pakistan. Similarly, Huston (2018) found that proactivity and innovativeness were antecedents of entrepreneurial intention in a sample of Doctor of Pharmacy students in a pharmacy practise management course at the University of Georgia. Castillo & Fischer (2019) also found that proactivity influenced the desire to be entrepreneurial in a sample of individuals with disabilities. In summary, proactive individuals will be more likely to have entrepreneurial intentions than others, as expressed below. H6: Proactivity is positively related to Entrepreneurial Intention. A growing body of research links creativity with the generation of new and valuable ideas (Amabile, 1996) with entrepreneurship (Mahmood et al., 2018). Creativity enables entrepreneurs to innovate, generate ideas and identify opportunities (Montañés-Del-Río 103 & Medina-Garrido, 2020). In this way, creativity is consolidated as a critical component of entrepreneurship (Schumpeter, 1934). Thus, the more creative individuals are, the more likely they are to be entrepreneurs (Hamidi et al., 2008). Mahmood et al. (2018) showed that the creativity of MBA graduates with experience in the labour market was an antecedent of entrepreneurial intention. Similarly, Zampetakis et al. (2011) also found a positive relationship between creativity and entrepreneurial intentions in a sample of 180 undergraduate students in England. This relationship was also supported in a sample of 484 undergraduate students in the work of Kumar & Shukla (2022). With these results, we can posit that creativity is positively related to entrepreneurial intentions. H7: Creativity is positively related to Entrepreneurial Intention. Program learning refers to the learner's knowledge during training courses (Souitaris et al., 2007). Numerous studies have found a positive relationship between program learning and entrepreneurial intentions. Rauch & Hulsink (2015) One study found that the entrepreneurship training program for undergraduate students at a university in the Netherlands positively influenced their entrepreneurial intentions. Similarly, empirical evidence that entrepreneurship education positively affects entrepreneurial intentions was found in a sample of university students from several Latin American countries (Leiva et al., 2021). The relationship between entrepreneurship education and entrepreneurial intentions was also positive in a sample of fourth-year undergraduate business administration students at the University of Granada in Spain (González-López et al., 2019). The above sources of evidence allow the following research hypothesis to be put forward. H8: Program Learning is positively related to Entrepreneurial Intention. In environments of uncertainty, entrepreneurs adopt more causal logic to take advantage of opportunities that arise the more information and knowledge they acquire about the evolving environment (Yao et al., 2013). The causal orientation involves the creative exploitation of opportunities and the development of business plans (Chandler et al., 2011; Sarasvathy & Dew, 2008). In addition, creativity is a helpful resource for recognising when it is necessary to make established plans more flexible (de Vasconcellos et al., 2019), planning being the basis of causal logic. Creativity also impacts the 104 propensity towards causal logic in decision-making at an early stage where the individual is not yet entrepreneurial. The creativity that makes it possible for entrepreneurs to seize business opportunities fuels their potential ability to generate better business plans to take advantage of them. Based on the above arguments, the following hypothesis captures the relationship between creativity and causal propensity. H9: Creativity is positively related to Causal Propensity. Based on the above arguments and the connections discovered above, we establish the research model shown in Figure 1. Figure 2. Research model Methodology Data collection The sample size was n=464 respondents. The sample was obtained with the voluntary collaboration of the students of the degree course in Tourism of the universities of southern Spain. They were interviewed employing a self-administered questionnaire using non-probabilistic sampling. The questionnaire was distributed between April and Entrepreneurial Intention Program Learning Causal Propensity Proactivity Creativity H1 H2 H3 H4 H5 H6 H7 H8 H9 105 July 2021. The questionnaire items were measured with a seven-point Likert scale: one strongly disagreed, and seven strongly agreed. The main socio-demographic characteristics of the sample are: in relation to gender, 71.02% of the respondents are female compared to 28.98% male. Concerning age, 56.04% of the respondents are between 18 and 21 years old, 37.58% are between 22 and 25 years old, and the rest are over 25 years old. The 97.4% are Spanish nationals, and only 2.6% are foreigners. Regarding entrepreneurial tradition, 31.82% of those surveyed belong to a family whose parents have had or have had their own business. Regarding their employment situation, 34.20% have worked or are currently working as employees, compared to 46.84% who have never worked; 18.96% have worked or are currently working as self-employed. Measurement scales The constructs of our model explain indicators that are highly correlated and interchangeable. It is, therefore, a composite model with reflective indicators. Causal propensity is an individual's tendency towards predictive logic based on his or her knowledge in making decisions before undertaking a business venture (Martín-Navarro et al., 2021). We used seven indicators from Martín-Navarro et al. (2021) to measure it. According to Amabile (1996), creativity is the ability to create new and valuable ideas. Creativity was adapted from Zampetakis (2008) and consisted of four items. Program Learning provides the context for learners to acquire skills and behaviours to create value in enterprises (Gundry et al., 2014). This construct was adapted from GUESSS questionnaire (Lechuga Sancho et al., 2020) and consists of five items. The individual's certainty to plan and create a new enterprise in the future (Thompson, 2009) defines Entrepreneurial Intentions. It comprises six items adapted from Liñán & Chen (2009). Moreover, proactivity, as the individual's active efforts to effect changes in his or her environment (Zampetakis, 2008), is measured with ten items from Seibert et al. (2001). All indicators are listed in Appendix 1. Data analytics In order to test the hypotheses, we used a structural equation model (Hair et al., 2014). The first objective of PLS-SEM is to determine the amount of variance explained in the endogenous variables of a structural model and explain the relationships posited in the 112 Hypotheses H5, H6 AND H7 of the model posited in this study propose that causal propensity, proactivity and creativity are antecedents of entrepreneurial intentions. The first two of these relationships are supported (H5 and H6). However, the third, H7, is not satisfied as the relationship between creativity and intentions is not significant. Our results are in line with those of Li et al. (2020), who also found that causal behaviour affected intentions to start a business. Similarly, our findings support the results of Sidratulmunthah et al. (2018), Huston (2018), and Castillo & Fischer (2019), who found that proactivity affects intentions. However, in our study, creativity is not a determinant of entrepreneurial intentions. This result is contrary to those of Kumar & Shukla (2022) y Zampetakis et al. (2011). The previous authors did find empirical evidence for the relationship between creativity and entrepreneurial intentions. Similarly, Zampetakis (2008) demonstrated that proactivity and creativity affect intentions among the Greek students who participated in their research, mediated by the desire to be entrepreneurial. Many researchers have found the positive effect of program learning on entrepreneurial intentions (González-López et al., 2019; Leiva et al., 2021; Rauch & Hulsink, 2015). Our results confirm these findings as H8 is supported. Therefore, our sample finds that program learning is an antecedent of entrepreneurial intentions. Finally, we can affirm that creativity determines causal propensity since H9 is also satisfied. These results are consistent with the arguments of Chandler et al. (2011) and Sarasvathy and Dew (2008). These researchers claimed that creative ideas were part of the causal logic. By analogy, our results show that they are also antecedents and part of causal propensity. Conclusions There are many studies on students' entrepreneurial intentions (Barba-Sánchez et al., 2022; Leiva et al., 2021; Wang et al., 2022). Nevertheless, this type of study in the tourism sector is especially relevant due to the opportunities this industry offers for business development and generating wealth and employment. Our study analysed a sample of undergraduate tourism students at universities in southern Spain. This work proposed a research model with four variables as antecedents of entrepreneurial intentions (program learning, creativity, proactivity and causal propensity). The inclusion of causal propensity in this model is innovative as it is the first time it has been studied as an antecedent of entrepreneurial intentions. This variable refers to the tendency towards the causal logic of people who have not yet started their business. 113 The proposed theoretical model established nine research hypotheses. The results support eight of these hypotheses, and only one was rejected. Thus, three of the four determinants proposed in the research influence entrepreneurial intentions. These determinants are program learning, proactivity and causal propensity. However, according to the results of this study, there is no evidence that creativity directly influences entrepreneurial intention. The predictive capacity of the theoretical model established in this work is also remarkable. We have evaluated our model with PLSpredict, and the result shows that it has a high predictive ability, which indicates that similar results will be obtained in other samples with new cases. Contributions Given the results obtained, our work has important implications for academic research and practice. First, this study adds value to the literature on entrepreneurial intention as it is the first time that the impact of the causal propensity of individuals who have not yet started their business on their entrepreneurial intentions has been tested. Secondly, the sequential mediating effect of creativity, proactivity and causal propensity on the relationship between program learning and entrepreneurial intention has been tested. Our results also have implications for the educational community, policymakers, and other agents working to promote self-employment. The results of this study show that to promote entrepreneurial intentions, it is necessary to offer training in entrepreneurship that develops students' creativity and proactivity. In addition, students need to learn how to make business plans in which they plan the opportunities to be seized and the objectives to be achieved. By analogy, employers who wish to support intrapreneurship among their employees should also consider the importance of training that fosters creativity, proactivity and causal propensity to generate entrepreneurial intentions that encourage the development of projects within the organisation or in spin-offs. Limitations and future agenda This work has some limitations that also present opportunities for future research. First, the sample used in this study comprises tourism students from two universities that are very close geographically, so their responses do not differ too much. In this sense, we propose that the researchers carry out this study with students from other university 114 degrees and in different cultural contexts and countries. We find it particularly interesting to test our model on students undertaking an undergraduate degree in Social Work, in order to compare the results between traditional entrepreneurial intentions and those with a social entrepreneurship character. In addition, we invite other researchers to analyse the proposed model in non-university population samples. We also propose to study the moderating role of gender to determine whether there are differences in entrepreneurial intentions between men and women. Second, this paper only studies entrepreneurial intentions prior to the creation of the company. The theoretical model proposed in this paper could be completed by including the next stage of the entrepreneurial process, that is, the launch of the start-up company. To this end, we propose to study the conversion rate of potential entrepreneurs with entrepreneurial intentions into entrepreneurs who eventually create companies. It would also be interesting to test whether the initial causal propensity translates into a causal logic during the process of business creation. Third, the proposed theoretical model could be extended with other personal factors such as effectual propensity, passion, optimism, or risk aversion, among other variables. We could also include in the model the antecedents of entrepreneurial intentions from the theory of planned behaviour (Ajzen, 1991), namely subjective norms, personal attitude, and perceived behavioural control. Moreover, fourth another limitation is that the causal propensity construct lost several items in the factor analysis that were present in the original measurement scale proposed by Martín-Navarro et al. (2021). For that, additional effort is required to confirm the construct's consistency. 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Graduate entrepreneurship in the developing world: Intentions, education and development. Education + Training, 53(5), 325–334. https://doi.org/10.1108/00400911111147668 128 CAPÍTULO 5 Resultados y Discusión 129 Esta investigación se basó en el estudio de los determinantes que influyen en la intención emprendedora, destacando especialmente la propensión efectual y causal. A lo largo de este trabajo, se ha llevado acabo un análisis que abordó dos etapas diferenciadas. En primer lugar, una validación de la escala de medida utilizada para las dimensiones “propensión efectual” y “propensión causal”. En segundo lugar, pusimos a prueba las hipótesis formuladas en dos modelos de investigación mediante la aplicación de una técnica estadística multivariante. Esta técnica permite contrastar aquellos modelos que proponen relaciones causales entre las distintas variables. Son los modelos de ecuaciones estructurales o SEM (Structural Equation Modeling) (Ruiz et al., 2010). El uso de los SEM en el campos de ciencias sociales ha ido creciendo debido a que se trata de una herramienta muy útil que puede identificar relaciones entre las distintas variables de un modelo (Leyva & Olague, 2014). En este capítulo, presentamos los resultados obtenidos, que además de responder a nuestras preguntas de investigación, también han sido capaces de revelar perspectivas novedosas que contribuyen significativamente al cuerpo de conocimientos en este campo. Estos resultados no solo son la culminación de los esfuerzos realizados, sino también la apertura de nuevas líneas para futuras investigaciones y aplicaciones prácticas. Resultados de la validación de la escala de medida En la literatura académica se ha estudiado la lógica efectual durante el proceso emprendedor. Esto es, la teoría effectuation ha analizado el comportamiento de los emprendedores que ya han puesto en marcha su negocio. Sin embargo, no se ha analizado esta conducta en personas que aún no lo han hecho y se encuentran en una fase previa. En este sentido, esta investigación pretende desarrollar y validar un instrumento de medida que evalúe la propensión de los individuos hacia un comportamiento efectual o causal cuando aún no han creado una empresa, pero potencialmente podrían hacerlo. Para ello, se diseñó un cuestionario de 25 ítems, adaptado del trabajo de Werhahn et al. (2015), Chandler et al. (2011), y Gabrielsson et al. (2011), que mide dos variables: propensión efectual y propensión causal. La propensión efectual se compone de cinco dimensiones con sus respectivos indicadores: (1) Orientación a los Medios con tres indicadores; (2) Orientación a la Asociación con cuatro; (3) Orientación a la Pérdida Asumible compuesta por tres ítems; (4) Orientación a la Contingencia medida a través de cuatro indicadores; 130 y (5) Orientación al control compuesta también por cuatro. Por otro lado, la variable propensión causal se mide con siete indicadores. Los indicadores uno, tres y seis se adaptaron del trabajo de Chandler et al. (2011), mientras que el resto de indicadores que se han utilizado fueron adaptados de la investigación de Gabrielsson & Politis (2011), que diseñaron esos indicadores a partir de las recomendaciones de Sarasvathy (2001). Siguiendo a Dittrich, Francis, Hatzinger, & Katzenbeisser (2005) los anteriores ítems se midieron en una escala Likert de siete puntos, donde uno corresponde a “totalmente en desacuerdo” y siete a “totalmente de acuerdo”. Esta nueva escala puede contribuir a la construcción de teorías más sólidas de la intención emprendedora a través de la propensión efectual y la propensión causal. Para validar la nueva escala de medida, se utilizó una muestra de estudiantes universitarios de último curso de Administración y Dirección de Empresas de la Universidad de Cádiz y de la Universidad de Sevilla que, si bien aún no habían mostrado el comportamiento efectual o causal, sí podían presentar cierta propensión hacia el tipo de comportamiento que manifestarían si crearan una empresa. Para validar una escala que pueda medir si el estudiante tiene propensión efectual o causal, es importante estudiar esta propensión antes de que estos reciban formación específica en emprendimiento, ya que esa formación les pondría condicionar. Es por ello que el cuestionario se contestó en el primer semestre del curso académico, antes de que comenzara la asignatura “Creación de empresas”, gracias a la cual adquieren conocimientos y competencias para realizar un plan de negocios. La recogida de datos se llevó a cabo durante octubre y noviembre de 2018. Para dicha recopilación se creó un cuestionario online a través de la herramienta de formularios de Google (Hariguna et al., 2016; Jiang & Wu, 2016; Lian, 2017), que permite al usuario acceder a la encuesta a través de un enlace web. Esta herramienta vuelca los datos directamente en una hoja de cálculo, lo cual facilita su posterior tratamiento estadístico. Se obtuvo una muestra de 230 cuestionarios cumplimentados sobre un total de 463 estudiantes matriculados, obteniendo una ratio de respuesta del 49,67%. El 58,3% de los encuestados eran mujeres. El 86,1% de los encuestados tenía entre 18 y 24 años de edad, el 11,7% entre 25 y 30 años; y el resto era mayor de 30 años. El 58,3% eran estudiantes de la Universidad de Sevilla, mientras que el 41,7% estaban matriculados en la Universidad de Cádiz. El 67,4% pertenecían a la titulación de Administración y Dirección 131 de Empresas, mientras que el 32,6% al doble grado de Administración y Dirección de Empresas y Derecho. El 36,1% de los encuestados pertenecían a una familia cuyos padres han poseído o poseen un negocio. El 43,5% había trabajado o estaba trabajando por cuenta ajena, frente al 48,3% que nunca había trabajado; el resto había trabajado o estaba trabajando por cuenta propia o bien, por cuenta propia y ajena. Con el fin de validar el instrumento de medida se utilizó el software SmartPLS en su versión 3.2.7., que permite analizar las relaciones entre variables latentes y sus indicadores. Este análisis muestra si todos los indicadores representan su constructo correspondiente o si es necesario eliminar algunos de ellos. SmartPLS evalúa la medición de las variables en función de la fiabilidad individual, la fiabilidad de constructo, la validez discriminante y la validez convergente. Este análisis garantiza que los indicadores representan bien su variable correspondiente. Concretamente, la fiabilidad garantiza que la medición produce resultados coherentes, y la validez garantiza que los indicadores de un constructo miden sólo su constructo y no otro. En primer lugar, se analizó la fiabilidad individual de cada ítem mediante correlaciones simples de los indicadores con su respectivo constructo. Los resultados mostraron que no había indicadores con cargas inferiores a 0,4, límite establecido por Hair et al. (2011). Y, aunque se encontraron indicadores con cargas entre 0,4 y 0,7 que podrían eliminarse en una fase posterior, en esta primera etapa no se procedió a eliminar ningún ítem. En segundo lugar, analizamos la fiabilidad del constructo. Para ello, las medidas correspondientes a la fiabilidad compuesta deben ser superiores a 0,7 (Dijkstra & Henseler, 2015). En este estudio, todos los indicadores cumplieron este requisito excepto el indicador de Dijkstra-Henseler (ρA) en la dimensión "pérdidas asumibles", ya que su resultado fue de 0,691, que no superó el valor establecido de 0,7. Sin embargo, no se procedió a eliminar el indicador ya que el valor era muy próximo a 0,7 y cumplía el umbral de fiabilidad compuesto (que es una medida de mayor aceptación que el indicador ρA). Además, el resto de las cargas de los indicadores que componen esta dimensión eran bastante elevadas. Posteriormente, se estudió la validez convergente a través de la varianza media extraída (AVE), requiriéndose valores superiores a 0,5 (Fornell & Larcker, 1981). En este caso, la variable latente "Causation" explicaba menos del 50 % de la varianza de los indicadores que la componían. Por este motivo, se procedió a eliminar los dos ítems con menos carga 132 y como resultado, AVE alcanzó una puntuación aceptable por encima del 0,5 recomendado. Por último, analizamos la validez discriminante antes de eliminar los ítems anteriores. Siguiendo a Henseler et al., 2016, existe validez discriminante cuando la relación heterotrait-monotrait de correlaciones (HTMT) tiene valores inferiores a 0,85 (Gold et al., 2001). Todos los valores estaban por debajo de este umbral, por lo que no fue necesario eliminar ningún otro indicador. Tras la eliminación de los dos ítems de la variable propensión causal, volvimos a calcular todos los indicadores anteriores. Se cumplió la fiabilidad individual, ya que las cargas estaban dentro de los umbrales definidos anteriormente. También se cumplió la fiabilidad del constructo, ya que los valores de la fiabilidad compuesta superaron el límite de 0,8 y los resultados de ρA también fueron superiores a 0,7. En cuanto a la validez convergente, observamos que todos los valores de AVE superaban 0,5, por lo que cada ítem representaba adecuadamente un único constructo. Por último, tras la eliminación de los dos ítems, se comprobó que seguía existiendo validez discriminante, ya que los valores del cociente HTMT entre las distintas variables eran inferiores a 0,85. En definitiva, y tras analizar la fiabilidad individual, la fiabilidad del constructo, la validez discriminante y la validez convergente de estos constructos, obtuvimos una escala con 23 ítems fiables y válidos. De la escala de 25 ítems, eliminamos sólo dos del constructo propensión causal para aumentar la varianza explicada y mejorar la validez convergente. Los ítems eliminados fueron: "Trato de evitar situaciones inciertas en la mayor medida posible" y “Cuando me planteo objetivos a alcanzar analizo a mis competidores en profundidad”. Impacto de la Propensión Efectual en las Intenciones Emprendedoras El segundo trabajo integra el modelo de intención emprendedora con la lógica efectual. Para ello se ha desarrollado un modelo TPB extendido con la variable Propensión Efectual (EP). El modelo TPB extendido con EP puede considerarse como un modelo útil para predecir la intención emprendedora en el ámbito del turismo. El modelo de investigación se compone de nueve hipótesis. 133 Para este segundo trabajo se diseñó un cuestionario revisado por expertos y sobre el que se realizó una prueba piloto. El primer bloque recoge las variables demográficas y el resto se compone de 37 preguntas articuladas sobre una escala Likert de 7 puntos. A partir de la variable “propensión efectual” se procede a determinar la escala, con los indicadores o ítems necesarios para medir dicha variable no observable directamente por el investigador. Los indicadores del constructo “propensión efectual” fueron adaptados del trabajo de (Martín-Navarro et al., 2021) y se integran en cinco subdimensiones: (1) orientación a los medios que se mide a través de tres ítems, (2) orientación a la asociación se midió a través de cuatro (3) orientación hacia pérdidas asumibles se compone de tres indicadores (4) orientación a contratiempos de cuatro y, (5) orientación al control se ha medido con cuatro indicadores. Por otro lado, las cuatro variables restantes pertenecientes al TPB se midieron con el siguiente número de ítems: (1) actitud con cinco indicadores, (2) control del comportamiento percibido igualmente con 5 ítems, (3) las normas subjetivas se midieron con cuatro indicadores y, (4) la intención emprendedora con seis. Todos los indicadores fueron adaptados de la escala de Liñán & Chen (2009). Se consiguió una muestra de 464 cuestionarios correctamente cumplimentados entre estudiante del grado de turismo de la universidad de Cádiz y de la universidad de Sevilla. En relación con la muestra, el 71,02% de los encuestados son mujeres frente al 28,98% de hombres. Entre 18 y 21 años se encuentra el 56,04% de los casos, el 37,58% tienen entre 22 y 25; y el resto es mayor de 25 años; la mayoría son españoles, sólo el 2,6% son extranjeros. El 56,71% son estudiantes de la Universidad de Sevilla, mientras que el 43,29% están matriculados en la Universidad de Cádiz. El 31,82% de los encuestados pertenecen a una familia cuyos padres han poseído o poseen algún tipo de negocio. Y finalmente, de 34,20% ha trabajado o está trabajando por cuenta ajena frente al 46,84% que nunca ha trabajado; el resto ha trabajado o está trabajando por cuenta propia o bien, por cuenta propia y ajena. Para el análisis del modelo de investigación se ha utilizado el software SmartPLS 3 (Ringle et al, 2015). Este análisis se realizó en dos fases: el análisis del modelo de medida y el análisis del modelo estructural. En la primera fase se estudia las relaciones de las variables latentes con sus indicadores. En la segunda fase y tras analizar la fiabilidad y validez debe evaluarse el modelo estructural para identificar las relaciones entre los constructos del modelo de investigación propuesto (Velicia Martín et al., 2020). 134 Análisis del Modelo de Medida En relación con la fiabilidad individual de los ítems, debe comprobarse que las cargas son superiores a 0,707 (Carmines & Zeller, 1979). En nuestro estudio, “orientación hacia los medios” y “orientacióna hacia las pérdidas asumibles”, subdimensiones del constructo EP, tienen una carga inferior y, por lo tanto, se eliminarán del análisis. Posteriormente, se analiza la fiabilidad del constructo a través de Dijkstra-Henseler rho_A en todos los casos con valores superiores a 0,7 (Dijkstra & Henseler, 2015). La fiabilidad compuesta (Werts et al., 1974) debe ser superior a 0,8 como sugieren Nunnally & Bernstein (1995). Para completar este análisis, se comprueba la validez convergente mediante la Varianza Media Extraída (AVE). En este caso, los valores deben ser superiores a 0,5 (Fornell & Larcker, 1981) y todos lo cumplen. El análisis de validez discriminante se realiza mediante el Ratio Heterotrait-Monotrait (HTMT) (Henseler et al, 2016). Esta ratio debe tener valores inferiores a 0,85 (Gold et al., 2001) para garantizar que se satisface la validez discriminante, lo cual muestran los resultados. En el constructo de segundo orden, EP, se elimina la dimensión "orientación a las pérdidas asumibles" porque las cargas son inferiores a 0,707 y la dimensión "orientación a los medios" por el mismo motivo. Una vez depurado el modelo de medida, continuamos con el análisis del modelo estructural. Una vez depurado el modelo de medida, y comprobado que las medidas son fiables y válidas, se continúa con la segunda fase, el análisis del modelo estructural teórico propuesto. Análisis del Modelo Estructural El análisis del modelo estructural permitirá identificar las relaciones entre los constructos del modelo de investigación propuesto (Velicia Martín et al., 2020). Este análisis pone a prueba si las hipótesis y las relaciones de los constructos del modelo teórico son respaldadas (Henseler et al., 2015). La evaluación del modelo estructural implica la estimación de las cargas de los caminos o path loadings y los valores de R2. Para ello, se utiliza en SmartPLS una técnica de bootstrapping con 5.000 remuestreos con el objeto de contrastar las hipótesis planteadas. Los path loadings indican la fuerza de las relaciones entre las variables explicativas y las variables a explicar (Ramírez-Correa et al., 2019). 135 Los resultados demuestran que de las nueve relaciones propuestas en el modelo teórico son significativas siete de ellas. Se comprueba que no es significativa la hipótesis H5 que muestra la relación de SN (“normas subjetivas”) en PBC (“control del comportamiento percibido”), ni tampoco H6 por lo que SN no es determinante de EI (“intenciones emprendedoras”). Los valores de R2 indican la cantidad de varianza explicada por las variables exógenas (Ramírez-Correa et al., 2019). En nuestro caso, el constucto Intenciones Emprendedoras presenta un R2 moderado ya que se encuentra entre los valores 0,67 y 0.33 (Hair et al., 2014) (ver figura 1). Figura 3. Modelo estructural final Note: *p<0.05; **p<0.01; ***p<0.001; ns not supported. El ajuste del modelo global se realiza mediante los valores del residuo cuadrático medio estandarizado (SRMR), y nuestro modelo presenta un valor de 0,052 inferior al valor de referencia, por lo que presenta un buen ajuste (Henseler et al., 2014). Por último, se analiza el poder predictivo del modelo que permite comprobar la generalizabilidad del modelo a otras poblaciones. Se aplica un PLSpredict, un enfoque basado en muestras fuera de la nuestra, desarrollado por Shmueli et al. (2016). Siguiendo a Roldan & Cepeda (2017) con la técnica PLSpredictive, se obtuvieron resultados expresados a través de las variables RMSE (R Mean Square Error) y MAE (Mean Absolute Error) y Q2_predict. Para que el modelo presente poder predictivo el valor de Q2 debe ser mayor que cero y en nuestro caso, se comprueba que todos los valores son positivos. Del mismo modo, 136 obtuvimos valores positivos de RMSE y MAE, lo que también muestra una buena predictibilidad (Woodside, 2013). Por lo tanto, esta investigación demostró la validez predictiva, o predicción fuera de la muestra, del modelo propuesto para predecir los valores de los nuevos casos. Discusión Los resultados de esta investigación muestran que la Propensión Efectual es un determinante de las Intenciones Emprendedoras, tanto de forma directa como de forma indirecta a través de PA y el PBC. Del mismo modo, se confirma que tanto PA como PBC presentan una relación directa y positiva con la intención emprendedora en la muestra analizada. Estos hallazgos van en sintonía con los resultados de otros muchos autores en el área de emprendimiento (Krueger et al., 2000; Liñan, 2004; Soomro et al., 2018; Urban & Ratsimanetrimanana, 2019). E incluso con los de Ahamed & Limbu (2018) en su estudio de la intención de uso de tarjetas de crédito. EP no solo presenta una relación positiva y directa con EI, sino que además presenta efectos indirectos positivos y significativos con las variables PA y PBC que contribuyen a explicar la intención emprendedora. La relación directa entre SN y EI presenta una relación no significativa. Esto mismo ocurre en otros trabajos como el de Doanh & Bernat (2019) que no encontraron significativo el efecto de SN on EI entre estudiantes universitarios en Vietnam. Sin embargo, contrasta con otros estudios que si han encontrado esta relación significativa en las intenciones emprendedoras de los estudiantes (Al-Jubari, 2019; Ridha & Wahyu, 2017). Por otro lado, el efecto indirecto de la SN a través de la PA sí que es significativo tanto sobre EI como PBC. También destacar que el análisis IPMA muestra como la actitud es la variable exógena que más contribuye al efecto total de la variable endógena EI, seguida de EP. En contraposición, la variable que menos contribuye al efecto total es PBC. Y, por último, se puede afirmar que el modelo teórico analizado con una muestra de estudiantes del Grado de Turismo presenta un adecuado poder predictivo, por lo que los resultados obtenidos en nuestro análisis pueden generalizarse a otras muestras. Como se comprueba el modelo teórico presenta unas satisfactorias cualidades psicométricas. Además, el modelo TPB ha incrementado su poder predictivo con la variable EP. 137 Impacto de la Propensión Causal en las Intenciones Emprendedoras En el tercer trabajo se han analizado cuatro antecedentes a la intención emprendedora de los estudiantes de Turismo: Propensión Causal (CP, casual propensity), Proactividad, Creatividad y Programa de Apredizaje. Además, también se han analizado las relaciones entre las distintas variables planteando un modelo de investigación con nueve hipótesis. Para medir las variables, los indicadores del constructo Porpensión Causal fueron adaptados del trabajo de Martín-Navarro et al. (2021) y está compuesto por siete ítmes. La Creatividad está adaptado de Zampetakis (2008) y se compone de cuatro indicadores. El constructo Programa de Aprendizaje se adaptó del cuestionario GUESS y consite en cinco indicadores. Intenciones Emprendedoras se adaptó de Liñán & Chen (2009) y se compone de seis ítems. Y Proactividad está medido con 10 ítmes del trabajo de Seibert et al. (2001). Al igual que en el trabajo anterior, se utilizó el software SmartPLS para analizar tanto el modelo de medida como el modelo estructural. Análisis del Modelo de Medida El sesgo de método común (CMB), en el contexto de la investigación que utiliza PLSSEM, es un fenómeno que podría estar causado por el método de medición, normalmente los cuestionarios tipo Likert (Kock, 2015). El CMB es una amenaza grave porque el sesgo puede afectar a los hallazgos debido a errores sistemáticos (Schwarz et al., 2017). En el desarrollo de esta investigación hemos tratado de evitar el CMB y hemos aplicado el procedimiento estadístico sugerido por Kock (2015) para detectarlo. Cuando los coeficientes VIF son superiores a 3,3 es un indicio de colinealidad y, por tanto, el modelo puede estar contaminado por CMB. Los resultados muestran los coeficientes VIF que están claramente por debajo del límite de 3,3. Para el análisis del modelo de medida se utilizó el método PLS-SEM. Se analizó la fiabilidad individual de cada ítem y se eliminaron los ítems con cargas inferiores a 0,707 (Carmines & Zeller, 1979). Posteriormente, se analiza la fiabilidad de constructo mediante Dijkstra-Henseler rho_A en todos los casos los valores son superiores a 0,7 (Dijkstra & Henseler, 2015). La fiabilidad compuesta debe ser superior a 0,8 (Nunnally & Bernstein, 1995). La validez convergente se comprueba mediante la Varianza Media Extraída (AVE). Los valores deben ser superiores a 0,5 (Fornell & Larcker, 1981) (Fornell & Larcker, 1981). En este estudio todos estos requerimientos se cumplieron. 144 effectual propensity is proposed as an antecedent of entrepreneurial intentions, together with the rest of the TPB determinants (PA, SN and PBC). Our work has significant added value since we have yet to find any study in the literature that analyses the effect of effectual propensity on the entrepreneurial intentions of people who have yet started a business. The results of the hypothesis testing showed two critical findings. Firstly, and as the most important finding, this study has shown that effectual propensity influences entrepreneurial intentions. Moreover, that effectual propensity indirectly affects entrepreneurial intentions through attitude and perceived behavioural control. Finally, the third paper in this thesis aims to determine the impact of causal propensity on entrepreneurial intentions. To do so, once again, we frame the study in the university setting since many researchers study the entrepreneurial intentions of students throughout the world (Barba-Sánchez et al., 2022; Leiva et al., 2021; Wang et al., 2022). Moreover, in the tourism sector. Entrepreneurship is particularly relevant in this sector due to the opportunities that this industry offers for business development. Our research work is developed among tourism undergraduate students in universities in southern Spain. This study proposed a research model with four variables as antecedents of entrepreneurial intentions (Programme Learning, Creativity, Proactivity and Causal Propensity). Causal Propensity in this model is innovative since, to our knowledge, it is the first time it has been studied as an antecedent of entrepreneurial intentions. The proposed theoretical model established nine research hypotheses. The results support eight of these hypotheses, and only one was rejected. These results show that three of the four determinants proposed in the research influence entrepreneurial intentions. These determinants are learning programme, proactivity and causal propensity. In our proposal, causal propensity is established as a determinant of intentions and in turn, entrepreneurship education also affects causal propensity. This is because causal propensity determines the orientation of people who set goals and plan to reach them. This is precisely how entrepreneurship is currently taught to students at the universities in our study. They are trained in how to draw up business plans. In other words, they are taught more technical skills than personal skills. Likewise, causation is a logic that makes entrepreneurs more predictive and makes them achieve their goals by following the strategies set out. In this way, the student needs the programmes to develop the causal propensity. In this line, Proactivity and Creativity appear as Causal Propensity elements. Creativity allows for the generation of new strategies and plans. Furthermore, on the other 145 hand, proactivity responds to a behaviour that helps to execute planned tasks. Therefore, both ingredients are part of the Causal Propensity. Theoretical implications The theoretical implications of a doctoral thesis not only answer the research questions but also reflect how the research extends, modifies or enriches existing knowledge in the specific discipline. This study contributes to the literature in three crucial respects. First, the research has made significant progress in developing a specific scale to measure two new constructs in the literature: effectual and causal propensity. This scale is presented as a valuable and novel tool in the context of the literature on entrepreneurial intention. The relevance of this scale lies in its ability to address and analyse the initial stages of the entrepreneurial process. That is, it focuses on understanding and measuring the propensity of individuals towards entrepreneurial intention before a business is created. This is crucial, as it provides a deeper understanding of the motivations and decisions that drive individuals to embark on the path of entrepreneurship. This research contributes to the existing literature by developing a novel scale. It provides a valuable tool for researchers focusing on understanding and measuring entrepreneurial intention, especially in the early stages of the entrepreneurial process. Secondly, this study represents a significant advance in research on entrepreneurial intentions by incorporating a previously unexplored factor: effectual propensity. The novelty of this approach is that effectual propensity emerges as a critical determinant in the formation of tourism students' intentions to start a new business. By identifying and demonstrating the direct influence of effectual propensity on entrepreneurial intentions, the research offers a more complete and nuanced perspective on the factors influencing the decision to become an entrepreneur. This new determinant is positioned as a relevant antecedent, complementing the other three determinants already established in the Theory of Planned Behaviour (TPB). The results obtained reveal two fundamental findings. First, effectual propensity exerts a direct influence on students' entrepreneurial intentions. Second, it is found that effectual propensity also indirectly affects intentions through two essential components: attitude towards entrepreneurship and perceived behavioural control. This finding reflects the 146 complexity of the linkages within the entrepreneurial decision-making process. It underlines the importance of considering effector propensity as a critical element in future research on entrepreneurial intentions. Finally, this study marks a milestone by introducing and demonstrating the importance of causal propensity as a precursor of entrepreneurial intentions in people who have yet to become entrepreneurs. A distinctive aspect of this research lies in its exploration of variables little studied in the literature on entrepreneurial intentions, such as proactivity and creativity, and how they relate. This more comprehensive approach contributes to filling existing knowledge gaps and highlights the complexity of the factors that influence entrepreneurial intentions. The academic contribution of this study is manifested in formulating a behavioural model that incorporates personal characteristics such as creativity, proactivity and causal propensity, clearly indicating their relevance in the formation of entrepreneurial intentions. Furthermore, the research reveals the mediating role of creativity, proactivity and causal propensity in the relationship between learning programmes and entrepreneurial intention. This finding expands our understanding of how educational programmes influence entrepreneurial intentions and underscores the importance of nurturing and fostering these personal characteristics in training future entrepreneurs. Practical implications The results of this thesis also have several implications for the business environment. The new scale provides contributions to management and entrepreneurs. In this sense, the measurement of effectual and causal propensity, through a validated scale, will be of practical use for: (1) entrepreneurship promoters who need to guide entrepreneurs' behaviour in a way that is consistent with the environment (Futterer et al., 2017), (2) managers of established companies and entrepreneurs themselves as they can use it to self-assess and evaluate the members of their organisations to implement the most appropriate strategic orientation to influence organisational culture and employee behaviour (Werhahn et al., 2015), (3) entrepreneurs who want to measure the effectual propensity of their potential partners and employees, and (4) academic institutions interested in developing and guiding entrepreneurial potential. 147 On the other hand, the results of this research provide entrepreneurs with the ability to assess the effectual propensity of employees and business partners, enabling them to make decisions about those with whom they already have an established relationship, as well as those who require a prior assessment of their entrepreneurial orientation before entering into future commitments. In addition, an interesting perspective is raised for employers seeking to stimulate entrepreneurship within their teams. It is not only essential to offer training that fosters creativity, proactivity and causal propensity, but the relevance of this training in internal projects within the organisation or even in creating spin-offs is also highlighted. This orientation towards developing entrepreneurial projects within the company can benefit both the organisation and the employee's professional growth. Similarly, if an employer seeks to foster proactivity among its employees, it should train them with creativity programmes. In this way, cultivating creativity can significantly boost proactivity. It also underlines the importance of training in program learning for developing causal logic among employees. Combining this training with an orientation towards creativity and proactivity provides valuable guidance for employers seeking to cultivate a work environment conducive to innovation and entrepreneurship. Social implications This work also has some implications for society. Of particular use is the validated scale for the educational system. Academic institutions interested in developing the entrepreneurial potential of their students will have a validated measurement instrument to assess the initial propensity of their students towards effectual or causal behaviour. This starting point will be helpful when establishing the training content for them, allowing them to develop the logic (effectual versus causal) most innate to them. However, once the dominant logic of each learner has been identified, it will be necessary to train them in both logics so that they can discern, implement and combine them and thus improve their chances of success depending on the context in which they find themselves (Futterer et al., 2017). Furthermore, it is vital that universities and entrepreneurship institutions value and develop the effectual or causal orientation of individuals, especially when students enter the labour market. 148 Measuring effectual propensity will also be of practical use for public and private actors promoting entrepreneurship, as they can pre-assess potential entrepreneurs and guide them towards better environmental use and adjustment (Futterer et al., 2017). Our results allow differentiating between effectual and causal orientation of higher education students. This will make it possible to adjust curricula to contain more personalised entrepreneurship training and to strengthen the efficacy orientation of students who require it. Also, promoting public policies to encourage entrepreneurs in the initial stages of the entrepreneurial process. One of how the university emits knowledge to the market is by recruiting its students. For this reason, it is interesting to differentiate between the orientations of these individuals with entrepreneurship intentions. Thus, the labour market will be able to differentiate, according to its needs, the competencies, behaviours or orientations of the graduates it hires. It is interesting that universities, as well as institutions promoting entrepreneurship and companies, know how to value and develop the orientation of individuals. In short, our results have implications for the educational community, policymakers, and any other agent working on self-employment. On the one hand, because training and proactivity affect entrepreneurship intentions, it would be interesting to design training programmes that prepare future entrepreneurs in technical aspects. Nevertheless, on the other hand, programmes that improve student behaviour and direct them towards entrepreneurship. In this way, because proactivity influences entrepreneurial intentions, students must be encouraged and supported to be proactive. Therefore, curricula need to be renewed and focused on teaching these behaviours. They should be motivated to be entrepreneurs and thus contribute to the economy and society. Students should be involved in projects in contact with the business world, bringing them into the reality of the industry around them. Limitations and future lines of research The results obtained in this thesis must be interpreted with some limitations in mind. However, these limitations represent opportunities for future lines of research. Firstly, despite the predictive power of the models proposed, the validity of the findings of the three studies cannot be established based on this single study, so the results may not be 149 generalisable. Further empirical work would be necessary to refine the established measures. Another limitation is that the sample used consisted of students from two universities in the same country and from two geographically close universities, so their responses do not differ too much. Future research should contrast the effectual propensity and causal propensity scales and research models in different population segments and other countries and cultures, especially in different samples with different educational and cultural backgrounds and socio-demographic profiles. Secondly, our work only studies the intentions of students who have yet to have entrepreneurial experience. Therefore, researchers are encouraged to work on the stage after intentions, that is, behaviour towards entrepreneurship. Moreover, to study what affects the step from intention to entrepreneurial behaviour. Third, our research only studies the influence of some variables on entrepreneurial intentions. Future research should study other personal factors, such as passion, optimism or risk aversion, alongside effectual or causal propensity. Fourthly, another limitation to consider is that the causal propensity construct, in the factor analysis of the third paper, lost several items present in the original measure proposed by Martín-Navarro et al. (2021). This required additional effort to confirm the consistency of the construct. It would be interesting for other authors to check the consistency of the construct in other samples. 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