Direct and indirect effects of operant resources on co-creation experience: Empirical evidence from Airbnb consumers

Hastari, Revi; Adela, Zehan; Alkhair, Hanesman; Alexander Joseph Ibnu Wibowo Article Direct and indirect effects of operant resources on cocreation experience: Empirical evidence from Airbnb consumers Verslas: Teorija ir praktika / Business: Theory and Practice Provided in Cooperation with: Vilnius Gediminas Technical University (VILNIUS TECH) Suggested Citation: Hastari, Revi; Adela, Zehan; Alkhair, Hanesman; Alexander Joseph Ibnu Wibowo (2020) : Direct and indirect effects of operant resources on co-creation experience: Empirical evidence from Airbnb consumers, Verslas: Teorija ir praktika / Business: Theory and Practice, ISSN 1822-4202, Vilnius Gediminas Technical University, Vilnius, Vol. 21, Iss. 1, pp. 92-103, https://doi.org/10.3846/btp.2020.10683 This Version is available at: https://hdl.handle.net/10419/248009 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Copyright © 2020 The Author(s). Published by VGTU Press This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. *Corresponding author. E-mail: [email protected] Verslas: Teorija ir praktika / Business: Theory and Practice ISSN 1648-0627 / eISSN 1822-4202 2020 Volume 21 Issue 1: 92–103 https://doi.org/10.3846/btp.2020.10683 DIRECT AND INDIRECT EFFECTS OF OPERANT RESOURCES ON CO-CREATION EXPERIENCE: EMPIRICAL EVIDENCE FROM AIRBNB CONSUMERS Revi HASTARI1, Zehan ADELA2, Hanesman ALKHAIR3, Alexander Joseph Ibnu WIBOWO4* 1, 2, 3, 4School of Business and Economics, Universitas Prasetiya Mulya, Jl. BSD Raya Barat I, BSD City, Serpong, Tangerang 15339, Indonesia Received 20 October 2018; accepted 18 February 2019 Abstract. This study aims to empirically analyze the direct and indirect effects of operant resources on co-creation experience of Airbnb consumers. Specifically, this study examines operant resources’ impact on perceived benefits, trust, and co-creation experience. In addition, this study also investigates the effect of perceived benefits, and trust on co-creation experience. We managed to collect a sample of 201 respondents obtained through online surveys. Respondents were consumers who have used Airbnb service offerings. Data were gathered using a questionnaire developed on the basis of a literature review. A convenience sampling was adopted in inviting consumers to participate in this study. The hypotheses were analyzed using simple and multiple linear regression analysis. The results show that operant resources are proven to influence perceived benefits, and trust of consumers. Likewise, perceived benefits, trust, and operant resources have been shown to influence the co-creation experience. The novelty and the most important contribution of this study are that it has succeeded in proving empirically the existence of the Service-Dominant Logic (S-D Logic) perspective. Keywords: service-dominant logic, operant resources, co-creation experience, value co-creation, tourism. JEL Classification: D83, L83, L86, M31, Z32, Z33. Introduction Nowadays, we are able to observe the people’s changing lifestyle which becomes increasingly mobile. Specifically, this can be seen from the changes in the people’s way of life and behavior in spending time through various activities and hobbies, as well as in expressing opinions about something (Kotler 2002). Generally, these lifestyle changes cannot be separated from its connection with the development of internet technology. As is known, the existence and important role of the internet is increasingly felt to support humans in solving various complex problems. In business, internet development has changed the way managers and owners do business. Currently, online business models have developed very fast throughout the world. This business model is considered to be able to simplify, accelerate, and improve the efficiency of business processes, and is able to expand market reach. Airbnb is one company that has successfully developed a business model. As is well known, Airbnb is a company from the United States that offers convenience for residential owners (hosts) who want to rent their homes to other people who want to rent a room, house, or apartment, within a certain period. Airbnb offers rental rates that are cheaper than rental rates in conventional hotels. The choice of occupancy is relatively diverse because the residential property is owned by a residential owner, not Airbnb. Consumers (tourists) can experience a unique holiday experience through various activities with local residents. Consumers can also choose activities that suit their hobbies or interests. Interactions that occur between consumers and residential owners, and even with local residents can create a unique shared experience for each consumer. It means that consumers can actually engage or participate in the co-cre- ation activities (process) of value and unique experiences Business: Theory and Practice, 2020, 21: 92–103 93 together with producers (residential owner and Airbnb) and local residents. This is supported by Saarijärvi et al. (2013) who state that the main key in marketing and business management services is the value creation. In addition, an understanding of value creation, which previously focused on the company, is now increasingly emphasizing on the role of consumers in the process of value creation (Prahalad and Ramaswamy 2003, Vargo and Lusch 2004). This shift of view is one of the ideas in the S-D Logic perspective. This new perspective also reveals the exchange of intangible resources between consumers and producers, such as the exchange of knowledge, skills, abilities, and expertise. The above intangible resources are often referred to as operant resources. In summary, we want to analyze the co-creation experience from the consumer’s side. Furthermore, we attempt to empirically examine the relationship between co-creation experience and the four antecedents, namely operant resources, perceived benefits, and trust. This research actually continues and deepens the previous studies conducted by Alves et al. (2016), and Alves and Mainardes (2017). In contrast, our research wants to investigate more deeply in the context of Airbnb consumers based on the S-D Logic perspective. 1. Literature review 1.1. Operant resources Operant resources are specialized knowledge and skills, which will later become a competitive advantage of the company (Madhavaram and Hunt 2008). A person’s competence in the form of a set of knowledge and skills can be applied through an action for the benefit of their own or another organization (Vargo 2008; Vargo et al. 2008). In line with the perspective of S-D Logic, Flint et al. (2014) argue that operant resources are intangible and dynamic. For example, in the process of making pizza, operant resources are people who use their knowledge and ability to make pizza. In addition, operant resources are used to act on operand resources and other operant resources (Vargo and Lusch 2004). A study by Alves et al. (2016) found evidence that operant resources, such as customer education, customer expertise, and social capital, influence co-creation with companies. Today the development of internet technology has changed the way consumers get information about hotels, flights, or tourist destinations (Grissemann and Stokburger- Sauer 2012). Consumers (tourists) must face a new and different transaction process than it used to be so that they need new knowledge to be able to process the transaction. Li and Petrick (2008) stated that knowledge is a source of competitive advantage in the tourism industry. Therefore, integrating the knowledge and skills possessed by producers and consumers will increasingly enable the creation of value-in-context, namely the pleasure, satisfaction, and happiness of consumers and producers. 1.2. Co-creation experience According to Prahalad and Ramaswamy (2004), high-qu- ality interactions that allow consumers to gain unique cocreation experiences with companies are the keys to find new resources for competitive advantage. Therefore, the value must be made jointly by companies and consumers. Prahalad and Ramaswamy (2004) describe co-creation as shared value creation by producers and consumers. When a company only focuses on consumers and regards consumers as “kings” that are always true, they are not considered as co-creation. On the other hand, Agrawal and Rahman (2015) describe eleven role groups that consumers can have in the process of creating value creation. If consumers have one of these roles, then they have already done value creation processes or activities with the company. These roles include co-producers, co-distributors, co-promo- ters, co-manufacturers, co-consumers, the customer as experience creators, customer an innovators, co-ideators, co-evaluators, co-designers, and customers as co-testers. Co-creation helps consumers in building a shared service experience according to the customer’s situation. If the company provides good services or spoils consumers with luxurious services, then it also does not include cocreation. Co-creation is interpreting a problem and solving the problem together between the company and the consumer (Prahalad and Ramaswamy 2004). In addition, co-creation creates an experiential environment where consumers engage actively in dialogue and together with companies in building experiences tailored to the consumer desires. In this process, the product may be the same, but each consumer can have a different experience. Co-creation offers a variety of experiences for each customer and not only offers a variety of products (Prahalad and Ramaswamy 2004). The co-creation experience is centered on consumers who form value and interact with companies to form shared values (Prebensen et al. 2013). Creating a unique experience requires the participation of consumers and connections that connect consumers with that experience (Pine and Gilmore 1998, Shaw et al. 2011). Operant resources, such as customer education, customer expertise, and social capital, have been shown to influence co-creation with the company (Alves et al. 2016, Alves and Mainardes 2017). In addition, perceived benefits and trust have also been shown to influence the co-creation behavior (Alves and Mainardes 2017). In tourism, creating unique and memorable experiences for consumers is very important so that companies are able to compete in this industry (Grissemann and Stokburger- Sauer 2012). 1.3. Perceived benefits Perceived benefits are defined as benefits perceived by consumers and are antecedents of consumer participation 94 R. Hastari et al. Direct and indirect effects of operant resources on co-creation experience: empirical evidence... in value creation in a virtual community (Nambisan and Baron 2009). Similarly, Roberts et al. (2014) stated that perceived benefits can encourage participation in co-crea- tion activities. The greater benefits the customers perceive in their relationship with the company, the greater their level of participation in creative-based activities with the company (Alves and Mainardes 2017). According to Hsu et al. (2007), the formation of positive behavior between individuals can be due to knowledge sharing activities. The perceived benefits occur when individuals believe that they can improve relationships by offering knowledge they have. The perceived benefits also proved to influence cocreation behavior (Alves and Mainardes 2017). In tourism, benefits can be derived from how people work together to collaborate and use the resources they have in the field of tourism (Zhang et al. 2009, Yilmaz and Bititci 2006, Wynne et al. 2001) and later they will obtain benefits that create more effective value (Vargo et al. 2008). 1.4. Trust Morgan and Hunt (1994) have emphasized the important role of trust in the company. Trust is one of the core variables to show loyalty (Sirdeshmukh et al. 2002). The greater a person’s trust in the company, the greater their chances of wanting to maintain relationships and engage in business in the future. In addition, trust also ensures consumers get value from future business transactions with the same supplier (Aurier and N’Goala 2010). Trust can reduce risk in exchange, provide continuity in relationships, and maintain loyalty. Consumers are also more likely to give advice and make recommendations to their friends and relatives about the companies they trust (Flint et al. 2011). Trust has also been shown to influence the co-cre- ation behavior (Alves and Mainardes 2017) and intention to travel (Abubakar and Ilkan 2016). In addition, identity and strategy build stakeholder trust (Melewar et al. 2017). Yang et al. (2018) in their study of Airbnb services have shown that the cognitive trust-identity attachment building mechanism is more effective than affective trust-bond attachment depending on the emotional distance between the users and hosts. Trust was the foundation on which the sharing economy was built. Without trust, people would never invite strangers to live in their house or ride in their car (Leung et al. 2019). 2. Conceptual framework and hypotheses 2.1. Relationships between operant resources and perceived benefits Vargo and Lusch (2004) revealed that in the perspective of the S-D Logic, service is defined as the application of the competencies (knowledge and skills) possessed to provide benefits for themselves and others. According to Vargo and Lusch (2017), the main role of operant resources (knowledge and skills) is when these resources can act on other resources and form a benefit. Nowicki et al. (2018) assert that competency is a fundamental resource of corporate strategy formation where the incorporation of knowledge, skills, and capabilities can be used to create a value proposition. Based on the description above, we propose the first hypothesis as follows: H1: Operant resources owned by consumers affect the perceived benefits of Airbnb consumers. 2.2. Relationships between operant resources and trust Kalaignanam and Varadarajan (2006) convey about customer engagement in value chain management. Similarly, customers can provide input, such as money, time, effort, and skills, to participate in the prosumption process (Xie et al. 2008). Customer expertise can influence motivation, desires, and the amount of customer participation in service delivery through collaboration (Lusch et al. 2007). Besides, relationship portfolio management capability can prove fruitful for relationship marketing strategy. Like companies, consumers also have relational competencies that are useful in the establishment, development, and maintenance of successful relational exchanges. Furthermore, the composite operant resources have a positive influence on the firm in terms of relational outcomes (Madhavaram and Hunt 2008). According to Madhavaram et al. (2014), competency plays a role in the creation of relationship marketing strategies. Companies with higher competencies are better able to create customer relationship marketing strategies. Therefore, operant resources play an important role in building relationship quality, such as trust. Based on the explanation above, we propose the second hypothesis as follows: H2: Operant resources owned by consumers affect consumer trust in Airbnb. 2.3. The relationship between perceived benefits and co-creation experience According to Ennew and Binks (1999), consumers will participate if they benefit from relationships with the company. The greater the benefits perceived by consumers in their relationships with companies, the greater their level of participation in co-creation-based activities with the company (Alves and Mainardes 2017). Nambisan and Baron (2009) find that perceived consumer benefits are antecedents of consumer participation in value co-creation in virtual communities. Similarly, Roberts et al. (2014), propose that perceived benefits can act as motivations to participate in co-creation activities. Verleye (2015) concludes that the expected co-creation benefits determine the importance of Business: Theory and Practice, 2020, 21: 92–103 95 the level of customer role readiness, technologization, and connectivity for the co-creation experience. The expected co-creation benefits that customers actually get in return for co-creation determine their overall co-creation experience. Based on the explanation above, we propose the third hypothesis as follows: H3: Perceived benefits affect co-creation experience of Airbnb consumers. 2.4. The relationship between trust and co-creation experience The concept of trust and value co-creation is closely related because the purpose of interaction and business relationships is value creation (Vargo 2009). Trust provides assurance regarding the consistency and competency of the company’s performance and ensures that consumers continue to get value from future business transactions with the same company (Aurier and N’Goala 2010). Trust can be one of the factors influencing the potential for value creation (Nahapiet and Ghoshal 1988). When a relationship has a high level of trust, the parties involved will be more willing to engage in a social exchange. According to Alves and Mainardes (2017), trust has been shown to influence the co-creation behavior. Similarly, consumers will show a higher level of co-creation activity if: (i) the consumer has trust in the company; (ii) these consumers feel that they can benefit from exchanging experiences with other consumers; and (iii) these consumers feel empowered with tools and resources that enhance their perception of self-efficacy. Based on the description above, we propose the fourth hypothesis as follows: H4: Consumer trust in the company influences co-cre- ation experience of Airbnb consumers. 2.5. The relationship between operant resources and co-creation experience According to Vargo et al. (2008), companies will use their understanding and capabilities in carrying out production activities and product branding. In addition, consumers also apply their own understanding and abilities in their daily use. The resources that consumers have are the most important foundation for the company in doing co-creation. Therefore, consumers are one of the most valuable strengths that a company has (Lusch and Vargo 2006). Similarly, Auh et al. (2007) argued that the ability of consumers is not only limited to their participation in service production, but it also involves a higher level of expertise to be able to participate in service production. Study Alves etal. (2016) asserted that operant resources, such as customer education, customer expertise, and social capital, have been shown to influence co-creation with the company. Based on the above explanation, we propose the fifth hypothesis as follows: H5: Operant resources owned by consumers affect Airbnb’s consumer co-creation experience. Overall, the relationship between variables as outlined in the conceptual framework above is summarized in the conceptual model below (see Figure 1). 3. Research methods 3.1. Research type This research is a descriptive study in which data collection is done using a survey questionnaire. Descriptive method is used to examine a group of people, objects, conditions, and events that occur in the present (Malhotra 2010). The purpose of this descriptive research is to obtain a description of the facts, properties, and relationships between phenomena that are being systematically investigated. Specifically, we will analyze the relationship between a number of variables, such as operant resources, perceived benefits, trust, and co-creation experience. 3.2. Population and sample The population is an aggregate of all elements that share some common characteristics for the purpose of marketing research problems (Malhotra 2015). The population of this study is all consumers or Airbnb service users who live in Jakarta, Bogor, Depok, Tangerang and Bekasi (Indonesia). The sample is a subgroup of the population chosen to participate in the study (Malhotra 2015). The sample of this research is Airbnb consumers who have made a transaction at least once, both women and men, aged between 18 and 38 years. 3.3. Sampling technique We used convenience sampling to gather data because there is no reliable sampling frame with which to conduct a random sampling. Convenience sampling is a sampling technique that is carried out by taking the easiest elements of the population. In this technique, researchers have the freedom to determine members of the population that are considered easy to be chosen as respondents (Malhotra Figure 1. Conceptual model 96 R. Hastari et al. Direct and indirect effects of operant resources on co-creation experience: empirical evidence... 2010). A number of studies have identified a preference among authors for non-probability sampling methods (Wiese and Jordaan 2012, Poon and Rowley 2007, Albaum and Peterson 1984). Non-probability sampling design, such as convenience and judgmental, was extensively used, which is surprising considering the limitations inherent in such research. The adoption of probability samples was reported less frequently but experienced increasing use over time (Leonidou et al. 2010). 3.4. Operationalization of variables We tested the validity and reliability of the instrument through a pre-test involving thirty respondents (Malhotra 2010). We hope that the instrument applied to the actual sample will be valid and reliable. A number of indicators were corrected after obtaining input from the results of this pre-test. We use a number of indicators for each construct based on previous studies. The construct of co-cre- ation experience is measured by four indicators from Yi and Gong (2013). For the construct of perceived benefits, we use four indicators made by Chan et al. (2010) and Nambisan and Baron (2009). Furthermore, three indicators for the construct of trust are obtained from Kinard and Capella (2006). Finally, the construct of operant resources is measured by four indicators from Ojasalo (2001) and Bell and Eisingerich (2007). All of the above indicators are translated into Indonesian and adapted to the context of Airbnb. Each construct above is measured by a number of indicators or measurement variables using a seven-point Likert Scale from 1 “Strongly disagree” to 7 “strongly agree”. Completely, all indicators for each construct are presented in Table 1 below. 3.5. Validity and reliability test According to Malhotra (2015), the validity test aims to examine the extent to which the differences in the observed scale scores reflect the correct differences between objects on measured characteristics rather than systematic or random errors. Validity test results are measured by factor loading scores. An indicator is said to be valid if it has a factor loading value greater than 0.5 (Malhotra 2010). On the other hand, reliability testing refers to an understanding that the instrument used is reliable as a data collection tool and refers to the extent to which the scale produces consistent results if the measurement is repeated (Malhotra 2015). The reliability for each construct was obtained using the Cronbach’s Alpha (CA) coefficient. The whole process of analysis to test the validity and reliability of the instruments is done using SPSS. Acceptable levels of reliability depend on the objectives of the research project (Katerattanakul and Siau 1999). There are researchers who claim that a CA value of 0.7 is considered adequate (Davis 1995). Malhotra (2010) considers the value is reliable if the instruments have a Cronbach’s Alpha score greater than 0.6. According to Nunnally (1978), measurements with CA values equal to or greater than 0.70 Table 1. Operationalization of variables Constructs Indicators Codes Sources Co-creation Experience During service provision or whenever entering into a contract, I provide the appropriate and necessary information to ensure good service provision. CE1 Yi and Gong (2013) During service provision or whenever entering into a contract with the company, I have an agreeable attitude towards company members of staff. CE2 I give advice about the service to other consumers. CE3 I have a certain tolerance towards possible company service failures. CE4 Perceived Benefits I receive a higher quality service PB1 Chan et al. (2010), Nambisan and Baron (2009) Provides me with solutions to specific product usage-related problems. PB2 Enhances my knowledge about advances in products, related products, and technology. PB3 Gives me enjoyment from problem-solving, idea generation, etc. PB4 Trust I’m confident that the company and its employees will correctly provide the service. TR1 Kinard and Capella (2006)I trust the advice provided by this company and its employee. TR2 I believe this company and its employees worry about my needs. TR3 Operant Resources I have a good level of knowledge of service operation. OR1 Ojasalo (2001), Bell and Eisingerich (2007) I understand the benefits of this service. OR2 I understand the limitations of this service. OR3 I feel confident about the means of applying this service. OR4 Business: Theory and Practice, 2020, 21: 92–103 97 can be accepted or can be called reliable. However, in a preliminary study, a CA score of 0.5 to 0.6 was considered quite reliable, and a CA score above 0.8 was considered difficult to obtain (Nunnally 1967). On the other hand, Lin (2010) states that the construct is said to be very reliable when the CA value is greater than 0.7; quite reliable when the value falls between 0.5 and 0.7; and the least reliable when the value is below 0.5. In fact, Mann and Rawat (2016) argued that a CA score of 0.5 was categorized as fairly reliable. According to them, the indicator can be removed if the CA score is less than 0.5. O’Donovan and McCarthy (2002) caution that the CA score in the initial survey can be satisfactory, but the CA score in the main survey can be unsatisfactory. Similarly, the fewer indicators, the lower the CA score (Frankforter and Guidry 2015, Nunnally 1967). The results of the factor analysis of 30 respondents in the pre-test showed that all indicators or measurement variables were proven valid because they had a factor loading value above 0.5. That is, all of these indicators have measured the construct that should be measured and can be trusted as a measuring tool. Similarly, all indicators are proven reliable because they have a Cronbach’s Alpha value above 0.6. Based on the results of the validity and reliability tests above, all instruments do not need to be repaired and they can be distributed to the actual respondents. 4. Data analysis techniques 4.1. Descriptive analysis In this study, we measured several descriptive statistics, such as frequency, mean, and standard deviation. Some demographic variables were analyzed, such as gender, age, residence, and expenditure. In addition, all variables in the research model were also analyzed using descriptive statistics, such as operant resources, perceived benefits, trust, and co-creation experience. 4.2. Regression analysis We use regression analysis techniques to test the five hypotheses proposed. There are two types of regression analysis, namely simple linear regression and multiple linear regression. Regression analysis is a statistical procedure to analyze the associative relationship between one dependent variable with one or more independent variables (Malhotra 2015). Before conducting a regression analysis, we test the classical assumptions first. The purpose of this test is to ascertain whether a number of criteria have been met before applying regression analysis. There are two classical assumption tests that we do, namely normality test and multicollinearity test. Specifically, the normality test is applied to simple and multiple linear regression models, while the multicollinearity test is applied only to multiple linear regression models. The normality test is conducted to find out whether the research variables have normal distribution or not. The normality test is done by analyzing the histogram and normal probability plot. The model fulfills the assumption of normality if the histogram shows normal distribution, and the p-plot graph shows the spread points around the diagonal line and follows the direction of a line (Ghozali 2016). On the other hand, a multicollinearity test was conducted to determine whether there was a multicollinearity trend in multiple linear regression models. Symptoms of multicollinearity are indicated by a significant correlation between independent variables. Multicollinearity test is done by looking at tolerance values and Variant Inflation Factor (VIF). If the tolerance score is greater than 0.10 or VIF is less than 10, multicollinearity does not occur between the independent variables (Ghozali 2016). In summary, the mathematical formula for Model 1 and 2 is presented below: = +b + 1 11 Ya Xe ; (1) = +b + 2 22 Ya Xe , (2) where 1 Y is perceived benefits; 2 Y is trust; 12 and XX are operant resources; a is constant; b i is beta coefficient for i X ; e is erroneous. Next, a mathematical formula for Model 3 is presented below: = +b +b +b + 1 11 22 33 Ya X X Xe , (3) where: 1 Y is co-creation experience; 1 X is perceived benefits; 2 X is trust; 3 X is operant resources; a is constant; b i is beta coefficient for i X ; e is erroneous. Similarly, we calculate the coefficient of determination 2 (R ). The coefficient of determination is used to measure the ability of the model to explain the variation of the dependent variable. This value measures how much influence of the independent variable has on the dependent variable. The value of a small determination coefficient means the ability of the independent variable to explain the dependent variable is limited. Conversely, if the coefficient of determination shows a large value, it means that the variable provides sufficient information to predict the dependent variable (Ghozali 2012). 5. Results 5.1. Profile of respondents and descriptive analysis We managed to obtain data from 201 respondents. The majority of respondents were Airbnb consumers who were male, over 25 years of age, residing in South Jakarta, with varying personal expenses of up to six million rupiahs per month. In summary, the profile of respondents can be seen in Table 2. In general, the average score for the four co-creation experience indicators is greater than five. This shows that consumers have gained co-creation experience with residential owners or Airbnb. Specifically, consumers have 98 R. Hastari et al. Direct and indirect effects of operant resources on co-creation experience: empirical evidence... Finally, the mean value of the four operant resources indicators is also greater than five. This indicates that consumers know the benefits to be gained by using Airbnb and feel confident in using Airbnb service. In addition, consumers have a good level of knowledge in operating Airbnb and every limitation found on the service offered by Airbnb. Moreover, the average value and standard deviation of all indicators can be seen in Table 3. Table 3. Results of descriptive statistical analysis, validity test, and reliability test Variables Mean SD Factor loadings Cron bach’s alpha Co-creation experience (CE) 0.563 CE1 6.16 0.654 0.785 CE2 6.30 0.70 0.724 CE3 5.59 1.129 0.646 CE4 4.93 1.280 0.580 Perceived benefits (PB) 0.748 PB1 5.92 0.835 0.532 PB2 5.58 1.051 0.817 PB3 5.65 1.058 0.815 PB4 5.32 1.053 0.819 Trust (TR) 0.782 TR1 5.88 0.778 0.836 TR2 5.76 0.802 0.861 TR3 5.60 0.944 0.817 Operant re sources (OR) 0.774 OR1 5.65 0.974 0.786 OR2 5.88 0.752 0.827 OR3 5.56 1.053 0.773 OR4 5.70 0.883 0.816 5.2. Results of validity and reliability test Based on the results of the validity test, we found that all indicators in this study were valid. The factor loading value varies from the lowest of 0.532 (PB1) to the highest of 0.861 (TR2). Likewise, the reliability test results prove that all instruments are reliable, except the co-creation experience which has a Cronbach’s Alpha value of less than 0.6, which is 0.563. Read more, the results of the validity and reliability test can be seen in Table 3. The results show that all instruments are reliable, including co-creation experience which has the lowest CA score (0.563). This construct can be categorized as quite reliable because it has a CA value between 0.5 and 0.7 (Nunnally 1967, Lin 2010). This is also supported by Mann and Rawat (2016) that a CA score of 0.5 is categorized as fairly reliable. Therefore, we accept or maintain all constructs and indicators, including co-creation experience, and then we carry out variously advanced analyses, such as regression. Table 2. The demographic profile of respondents Description Frequency Percentage Sex: Female 114 56.7 Male 87 43.3 Age: <25 years 168 83.6 26–35 years 33 16.4 Residence: South Jakarta 63 31.5 Tangerang 44 22 West Jakarta 30 15 North Jakarta 15 7.5 Bekasi 15 7.5 East Jakarta 13 6.5 Central Jakarta 8 4 Bogor 6 3 Depok 6 3 Expenditure (IDR million): <1 4 2 1–2 23 11.4 2–3 42 20.9 3–4 49 24.4 4–5 34 16.9 5–6 17 8.5 >6 32 15.9 been friendly to the residential owner and provided the information needed to get good service during the service or whenever there is contact with Airbnb. Similarly, consumers advise other consumers about Airbnb service and can be tolerant of service failures. Furthermore, the average value for the four perceived benefit indicators is also greater than five. This shows that consumers have felt the benefits gained from Airbnb service. Consumers feel that they receive better quality service during relationships with Airbnb. In addition, consumers feel that interactions with other Airbnb users are able to increase knowledge about Airbnb and are able to provide solutions to certain problems related to Airbnb usage, and are able to help consumers in problem-solving, idea formation, and others. The average value for the three indicators of consumer confidence is also greater than five. This shows that consumers have believed that Airbnb and residential owners can provide appropriate service. In addition, consumers also trust the advice provided by Airbnb and residential owners, and occupancy owners are considered to care about their needs. Business: Theory and Practice, 2020, 21: 92–103 99 5.3. Results of regression analysis The classical assumption test that we use is the normality test and multicollinearity test. The normality test is done by analyzing the histograph and probability plot. Overall, we found that the data had met the assumption of normality. This can be seen from a histogram showing a bell-like shape. In addition, a probability plot also shows a view where the points spread following a diagonal line from the zero points and do not widen too far from the diagonal line. After testing for normality, we conducted a multicollinearity test. The results of the analysis indicate that there are no symptoms of multicollinearity. This can be seen from the tolerance value greater than 0.10. Based on the results of the classical assumption test above, then we conduct a regression analysis to test the hypotheses that we propose. A simple linear regression analysis was used to test 1 H , which tested the effect of operant resources on perceived benefits. The result showed that operant resources (β = 0.379, p < 0.001) significantly affected perceived benefits. Therefore, 1 H was not rejected. The coefficient of determination for 2 R value for the predicted variable was 0.139 (above the critical value 0.1), which could be considered to indicate a substantial level of explanation (Schroer and Hertel 2009). It means that as much as 13.9 percent of the perceived benefits could be explained using the operant resources, while the rest (86.1 percent) was explained by other variables not examined in this study. Likewise, a simple linear regression analysis was used to test 2 H , which tested the effect of operant resources on consumer trust. The result showed that operant resources (β = 0.530, p < 0.001) significantly affected consumer trust. Therefore, 2 H was not rejected. The coefficient of determination for 2 R value for the predicted variable was 0.277 (above the critical value 0.1), which could be considered to indicate a substantial level of explanation (Schroer and Hertel 2009). It means that as much as 27.7 percent of the consumer trust can be explained using the operant resources, while the rest (72.3 percent) was explained by other variables not examined in this study. Finally, the multiple linear regression analysis was used to test 34 5 H , H and H , which tested the effect of perceived benefits, trust, and operant resources on co-creation experience (Table 4). The result showed that perceived benefits (β= 0.341, p < 0.001), trust (β = 0.247, p < 0.001), and operant resources (β = 0.173, p < 0.05) significantly affected co-creation experience. Therefore, 34 5 H , H and H were not rejected. The coefficient of determination for 2 R value for the predicted variable was 0.359 (above the critical value 0.1), which could be considered to indicate a substantial level of explanation (Schroer and Hertel 2009). It means that as much as 35.9 percent of the co-creation experience could be explained using the perceived benefits, trust, and operant resources, while the rest (64.1 percent) was explained by other variables not examined in this study. 6. Discussions According to the S-D Logic perspective, consumers play an active role in the value creation process or activity. Likewise, exchanges or transactions are carried out in the form of an exchange of processes or activities (not a unit of output), namely the exchange of knowledge and skills (operant resources), which is also called the service for service exchange. S-D Logic perspective defines service (singular) as an application of competencies, knowledge, and skills to provide benefits to other parties. Goods (unit of output) do not become the basis of exchange, but only become “vehicles” or tools (intermediaries) to deliver service. In the case of Airbnb, the place of residence, rental housing or housing that is rented by consumers is a tool (intermediary) to deliver service to consumers. S-D Logic lens views consumers, not as targets, but actors who actively integrate their resources to create value together with other actors who receive benefits (the beneficiaries) from service exchanges. So, the value creation process is carried out jointly by all actors involved and beneficiaries of this exchange. In the case of Airbnb, the actors involved and benefited were at least three, namely consumers, Airbnb, and house owners (host). These actors will exchange each operant resources to obtain new operant resources from other actors and also value in context, such as pleasure, satisfaction, happiness, or others, which are unique to each actor. In the context of Indonesia, Arifina and Ayu (2018) have conducted studies on Airbnb against local hosts in Table 4. Results of regression analysis Model Relationships btConclusions 1Operant resources  Perceived benefits 0.379*** 5.778 Not rejected 2Operant resources  Trust 0.530*** 8.815 Not rejected 3Perceived benefits  Co-creation experience 0.341*** 5.351 Not rejected 3Trust  Co-creation experience 0.247*** 3.521 Not rejected 3Operant resources  Co-creation experience 0.173* 2.535 Not rejected * r<0.05; ** r<0.01; *** r<0.001