Agility in marketing teams: An analysis of factors influencing the entry decision into a trendy social network

Agility in marketing teams: An analysis of factors influencing the entry decision into a trendy social network Itziar Oltra * , Carmen Camarero , Rebeca San Jos´ e University of Valladolid, Facultad de Ciencias Econ´ omicas y Empresariales, Avenida del Valle Esgueva, 6 – 47011, Valladolid, Spain ARTICLE INFO Keywords: Social media marketing Marketing agility Social media adoption UTAUT Fuzzy-set Qualitative Comparative Analysis (fsQCA) ABSTRACT Given the rise of new social networks, companies must decide whether to incorporate each new network into their social media marketing strategy. This research analyses the factors that influence a brand’s entry into a trendy social network, integrating two traditional paradigms of innovation adoption –the TOE and the UTAUT– with the concept of marketing agility, to incorporate the strategic perspective of marketing departments. We conduct a mixed methods approach, through a focus group with managers and a quantitative analysis based on a questionnaire with a sample of 161 managers, complemented with a fsQCA to identify specific configurations of factors that determine that entry. The study validates marketing agility’s relevance, emphasising the importance of market monitoring beyond speed. Three company characterizations are proposed, including differences in expectations, effort perceptions, and competitors influence. We offer an explanatory model of the adoption conditions of technological innovations undertaken by marketing departments, applicable to future innovations in communication tools. 1. Introduction The global discourse surrounding the exponential growth of social media usage and its profound impact on businesses has been ongoing for years. Social media has become an indispensable element for any brand, and strategies developed in social networks are now key pillars for the growth of most brands in the online environment (Kumar et al., 2016; Marchand et al., 2021). Yet the portfolio of available social networks is by no means static, since new ones are constantly emerging and because they experience different levels of growth. The most recent one to experience considerable growth and to position itself as one of the main social networks in terms of size is TikTok, which was born in 2017 and which is now surpassed only by Instagram and Facebook (Table 1). It boasts over one billion active users (The Guardian, 2022) and was the most installed app in 2022, with 672 million downloads (Statista, 2022). Whereas the decision faced by brands a few years ago concerned whether or not to venture into the realm of social media on a general scale, the current challenge lies in selecting which specific platforms to participate in once a general presence has been established. Parallel to this, previous literature has focused on exploring social media marketing as a new tool to be incorporated by a firm’s business communication strategy. Consequently, the process of adopting social networks for the first time has been extensively studied (Aspasia and Ourania, 2014; Dahnil et al., 2014; Kumar et al., 2019; Shaltoni, 2017; Siamagka et al., 2015), and the strategies developed and their effectiveness (Balaji et al., 2023; Felix et al., 2017; Godey et al., 2016; Lipsman et al., 2012) have been analysed from an operational point of view. Thus, the question of deciding whether or not to be present in social networks is no longer relevant, given that the latter have established themselves as an indispensable tool (Forbes, 2023). Having recognized companies’ generalized adoption and the relevance of social networks in business results, and considering the constant emergence of new social networks, academic interest must now go a step further and evolve towards the current business reality (Dwivedi et al., 2021). The key question now lies in the ability to discern whether a new social network fits in with a company’s strategy, whether it makes sense to adopt it, and whether allocating resources to it is justified. Furthermore, the decision concerning whether or not to enter a new social network is framed within the topic of exploring how useful information technologies are as a source of competitive advantage and can therefore be considered a research priority (Mikalef and Pateli, 2017). However, this crucial aspect about social media marketing still lacks the necessary research attention: hence the importance of addressing this research gap in order to contribute to the ongoing literature on social media * Corresponding author. E-mail addresses: [email protected] (I. Oltra), [email protected] (C. Camarero), [email protected] (R. San Jos´ e). Contents lists available at ScienceDirect Journal of Business Research journal homepage: www.elsevier.com/locate/jbusres https://doi.org/10.1016/j.jbusres.2024.115054 Received 22 September 2023; Received in revised form 5 November 2024; Accepted 5 November 2024 Journal of Business Research 187 (2025) 115054 Available online 14 November 2024 0148-2963/© 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC license ( http://creativecommons.org/licenses/bync/4.0/ ). marketing. The current paper therefore looks at the combination of factors that will lead companies who already have a social media strategy to decide whether or not to enter a new social network. This is a challenging process, since it involves making decisions that imply adaptation at the departmental level in a context of uncertainty. The question to be addressed is therefore: under what conditions do marketing departments decide to enter a new social network? What do they value when making such a decision? Our aim is to explain the decision that brands take when considering entry into a new social network –with specific focus on TikTok– given the current growth it is experiencing (Fig. 1). Specifically, we seek to model this decision, establishing the key variables that wield significant influence over the entry decision, in order to be able to draw conclusions that allow brands to act with greater certainty when the new social network successor emerges. If we understand the emergence of a new social network as a technological innovation –a novel tool that brands can use to enhance or sustain their market presence– then the Technology-OrganizationEnvironment (TOE) offers a general analysis framework, while the UTAUT (Unified Theory of Acceptance and Use of Technology) model provides specific variables to understand adoption processes (Venkatesh et al., 2003). However, while this model proves effective in examining technology adoption, it overlooks the possible impact of characteristics related to organizational aspects that may prove to be pivotal (Dahnil et al., 2014). The characteristics of marketing departments –in terms of their ability to listen to and respond to changes in the environment and the market– have a significant impact on decision-making. This influential factor is referred to in the marketing literature as the concept of marketing agility, and we propose incorporating it in order to capture its impact on the decision-making process analysed. The lack of general research exploring entry into a new social network as an innovation in company communication leads us to propose this conceptual framework for our research. In addition, by applying qualitative analysis through focus groups, we validate the relevance of these concepts and tailor them to our specific research context. This study is relevant from both a theoretical and a practical point of view. Theoretically, this research aims to propose a useful model that can effectively predict the strategic decisions of companies entering social networks –thereby contributing to the existing literature. Moreover, by introducing the concept of marketing agility, we innovatively propose an extension of the UTAUT model that fits in better with companies’ technology adoption than its previous versions and, more specifically, in marketing departments. From a broader perspective, this research is also relevant because of its contribution to developing the general literature on social networks and to current understanding of social networks as a strategic tool that may have a significant impact on business results (in contrast to the tactical approach of previous literature). From a practical standpoint, this modelling approach seeks to provide marketing departments with actionable insights, allowing them to identify and focus on key internal aspects that are crucial for successfully navigating and evaluating entry into future social networks (as well as other marketing tools) as they emerge. By establishing clearly defined criteria, marketing departments can effectively prepare and adapt to these evolving digital landscapes. The research is structured as follows. We begin with a comprehensive literature review on social media marketing, marketing agility, the TOE approach and the UTAUT model, and which serves as the theoretical foundation for the study. We then introduce the research methodology, with a justification for adopting the sequentially mixed methods approach. Subsequently, the article describes the studies carried out. The first study is qualitative and is based on the focus group technique. Table 1 Birth, active users, and growth of the principal social networks. Social Network Birth Monthly active users 2024 (millions) Growth worldwide 2022–2023 LinkedIn 2003 310 4.19 % Facebook 2004 3,049 2.30 % Twitter (now X) 2006 619 −3.90 % Instagram 2010 2,000 5.47 % TikTok 2016 1,562 10.50 % Source: own elaboration with data from Statista (2024). Fig. 1. Social network growth worldwide 2021–2024. I. Oltra et al. Journal of Business Research 187 (2025) 115054 2 In the second study, the hypotheses are justified, and a questionnaire is used to collect data for a quantitative analysis based on logistic regression is conducted. Finally, we employ a fuzzy-set Qualitative Comparative Analysis (fsQCA), providing a more in-depth and detailed explanation of the results obtained. Finally, the article concludes with a discussion section, presenting the study’s conclusions, limitations, and future research perspectives. 2. Literature review The use of social networks as part of companies’ digital strategies has gained increasing importance due to the former’s immense potential (Bannor et al., 2017). The concept of social media marketing refers to the use of social networks by companies or brands to achieve specific business objectives, with a focus on value creation within these platforms (Felix et al., 2017). One key characteristic of social media marketing is the ease and cost-effectiveness it offers brands in establishing connections with users (Kim and Park, 2013; Moe and Fader, 2004), enabling personalized one-to-one interactions (Li et al., 2023b). This enables interactivity, which is impossible through other media channels. Furthermore, the content generated by brands on social networks exerts a tangible impact on essential business-level metrics for the company, such as spending, cross-buying and profitability (Kumar et al., 2016). Social media literature has extensively examined social networks from five distinct perspectives (Li et al., 2023a): as a promotion and selling outlet (Hennig-Thurau et al., 2015; Rohm et al., 2013; Spotts et al., 2014), as a communication and branding channel (Choi et al., 2018; Zhang et al., 2017), as a monitoring and intelligence source (Feit et al., 2013; Moe and Schweidel, 2017; Schweidel and Moe, 2014), as a CRM and value co-creation platform (Heidenreich et al., 2015; Wang et al., 2016; Wang and Kim, 2017) and finally, as a general marketing and strategic tool (Brink, 2017; Mahmoud et al., 2020; Ryd´ en et al., 2015; Siamagka et al., 2015). Our research is situated within the framework of the latter approach, which highlights the strategic value of social networks within marketing strategy and their impact on company structure (Wu et al., 2020). From this perspective, the literature has focused on studying initial adoption or first entry on social networks as part of a company’s digital transformation process (Verhoef et al., 2021). A brand’s decision to adopt social networks is influenced by different factors that can affect this decision either positively or negatively (Felix et al., 2017). These factors have been explained by the Technology Acceptance Model (TAM) and the theory of resources (Siamagka et al., 2015), or in terms of organizational aspects such as a company’s sensemaking capacity (Ryd´ en et al., 2015). However, as yet there are no studies that combine aspects of the technology and specific variables that affect the behaviour of organizations and their departments. Moreover, there are no studies that analyse entry into a new social network when the brand already has a current social network strategy. This work aims to explore company adoption of social networks by extending the models of technology adoption to include departmental variables. While prior studies employ two distinct approaches –TAM and sensemaking– to explain general adoption in social networks, our study proposes a combined approach that considers the complementarity between innovation adoption models and marketing agility within a more general framework –the Technology-Organisation-Environment (TOE). Our work represents the first approach to the relevance of the concept of marketing agility vis-` a-vis the strategic communication decisions made by marketing departments. 2.1. Technology-Organization-Environment (TOE) approach and unified theory of acceptance and use of technology (UTAUT) An appropriate theoretical framework to study the incorporation of new information and communication technologies in companies is the Technology-Organization-Environment (TOE) approach proposed by Tornatzky and Fleischer (1990). According to this approach, the effectiveness of a business decision depends on its fit in internal and external factors, such that adopting a technology should take into account environmental, organizational, and technological factors. Technological factors refer to the characteristics of the technology that can influence the adoption process. Organizational factors are the characteristics of the organization, such as firm size, structure, or available resources and capabilities. Environmental factors are constraints and opportunities for technological innovations that stem from other actors –mainly industryor market-related factors (Wang et al., 2010). Although this general framework is considered appropriate to understand the decision to adopt an innovation (Abed, 2020; Dehghani et al., 2022), it does not specify specific factors or variables, but rather depends on the context in which the study is conducted (Wang et al., 2010). Based on this theoretical approach, we thus propose an explanatory framework for brand entry into new social networks grounded on the UTAUT approach, which fits in to the TOE approach. Since its formulation (Venkatesh et al., 2003), the unified theory of technology acceptance and use (UTAUT) has become the generalized model to explain the intention to adopt and the effective adoption of a technology. This model is able to group the eight main theories of technology acceptance: the technology acceptance model, the theory of reasoned action, the motivational model, the PC utilization model, the innovation diffusion theory, the theory of planned behaviour, a model that combines the technology acceptance model and the theory of planned behaviour, and the social cognitive theory. The UTAUT model posits four basic determinants of technology acceptance: effort expectancy, performance expectancy, facilitating conditions, and social influence. Performance expectations refer to the belief that the adoption and use of a particular technology will bring positive results (Brown et al., 2016; Venkatesh et al., 2003). For their part, effort expectations relate to the ease of use of the technology to be adopted (Venkatesh et al., 2003). Both effort and performance expectations are closely related, and lower effort expectations improve performance expectations in the online context (Chaouali et al., 2016). Facilitating conditions reflect the extent to which an infrastructure is considered to exist –in terms of organization and technology– that favours the use of the system to be adopted (Venkatesh et al., 2003). Finally, social influence refers to the degree to which an individual perceives that others believe that one should be using the tool or system (Venkatesh et al., 2003). It refers to the psychological principles that influence behaviour (Rashotte, 2007). Although there are subsequent updates of the model (UTAUT2 and UTAUT3), they focus on incorporating variables that provide value from the consumer perspective: hedonic motivation, price value, and habit in the case of UTAUT2 (Venkatesh et al., 2012), and personal innovativeness in UTAUT 3 (Farooq et al., 2017). The first UTAUT approach is therefore more suitable from the point of view of organizations. Moreover, applying the UTAUT model to predict an organization’s technology adoption is consistent with the TOE framework. The UTAUT model fits in with this proposal because effort expectancy and performance expectancy refer to technological factors; facilitating conditions allude to organisations’ characteristics, and social influence is an aspect that comes from the environment. In the specific case of company adoption of social networks, the UTAUT model has been used to explain the general adoption of social networks by small businesses (Humaid and Ibrahim, 2019), microbusinesses (Mandal and McQueen, 2012), and NGOs (Curtis et al., 2010; Lim et al., 2019). These studies analyse the decision to incorporate social networks into the firm’s marketing strategy from a situation where social networks were not previously used. Using UTAUT to explain the influential variables in this adoption makes sense to the extent that social networks can be considered an innovation –according to Rogers et al. (2014): “an idea, product, program or technology not used before by the organization”. I. Oltra et al. Journal of Business Research 187 (2025) 115054 3 2.2. Marketing agility Since the UTAUT model is mainly geared towards explaining the adoption of innovations by individuals, when we try to apply this model to a company’s adoption of a social network, we realize that it lacks the importance of the moment of adoption. Joining the market earlier or later –and doing so on the basis of a well-founded decision– may have a significant impact on either achieving or maintaining the company’s competitive advantage (Mikalef and Pateli, 2017; Rodríguez-Pinto et al., 2011)– hence the importance of taking into account the marketing team’s agility in this context (Carbonell and Rodríguez-Escudero, 2009). As a result, the concept of marketing agility is incorporated into the UTAUT model, which allows the required nuances to be added in order to apply the model from a business perspective. The concept of agility has received significant attention in the business literature. Starting from its classical definition applied to production (Yusuf et al., 1999), it has been adapted to different areas of business under a common premise: agility is based on the ability to detect and respond promptly to market changes. In the marketing area –where market dynamics and consumer preferences evolve rapidly (Syed et al., 2020)– agility has emerged as a critical factor for effective organizational functioning. Consequently, there has been a growing focus on agility from a marketing perspective. Kalaignanam et al. (2021) define marketing agility as “the extent to which an entity rapidly iterates between making sense of the market and executing marketing decisions to adapt to the market”. This definition allows marketing agility to be seen as a dynamic capability of the firm (Khan, 2020; Zhou et al., 2019). Building upon the work of scholars who have attempted to operationalize the components of marketing agility (Khan, 2020; Kalaignanam et al., 2021, Zhou et al., 2019), we can identify four fundamental components of marketing agility: sensemaking or proactivity, speed, responsiveness, and flexibility. Sensemaking or proactivity involves the ability to study and analyse ambiguous and uncertain contexts (Maitlis, 2005) employing continuous monitoring practices (Mu et al., 2018). Given this detection capacity, responsiveness emerges as another critical element in companies’ strategic marketing decision-making. Responsiveness goes one step further and is understood as the ability to adjust and respond to these emerging changes (Zhou et al., 2019), the ability to react and decide in the face of relevant stimuli that makes it possible to get it right and make a difference. In this process, speed plays a crucial role in facilitating prompt responses to opportunities identified through market monitoring (Zhou et al., 2019). Finally, flexibility within the scope of marketing agility refers to the ability to respond by efficiently choosing the best alternative to possible changes pinpointed in the market (Braunscheidel and Suresh, 2009; Grewal and Tansuhaj, 2001), and to do so iteratively (Kalaignanam et al., 2021). Although defined individually, these components collectively constitute the concept of marketing agility. In the area of social media, application of the marketing agility concept has been relatively limited. Existing studies tend to focus on its operational aspects, examining how it affects user engagement (Chuah et al., 2020) and customer-based brand equity (Gligor and Bozkurt, 2021). These studies approach the concept through specific constructs, such as social media agility (Chuang, 2020) and fan page agility (Chuah et al., 2020; Mandal et al., 2017). 3. Research methodology To bring us closer to the current business reality, the network that is always mentioned in studies is TikTok –which is justified by the growth this social network is currently experiencing (Guarda et al., 2021; He et al., 2021). Table 1 shows the year the main social networks were created, the total number of monthly active users in 2024, and the growth experienced between 2022 and 2023. As can be seen, TikTok is the youngest social network and has experienced the highest growth rate, while also having a high number of monthly active users (surpassed only by Facebook and Instagram). Additionally, Fig. 1 shows a breakdown of this annual growth from 2021 to 2024. While other social networks are experiencing small growth rates –with some even declining– TikTok stands out as the social network with the highest annual growth over the past four years. Its recent expansion means that it is at the same time the network with the most incipient attraction for research and also the one on which the least research has been done. TikTok content has been characterized since its emergence by its entertainment-based nature (Wang, 2020), especially through dancing (Haenlein et al., 2020), always using short videos –the only format supported by the platform (Haenlein et al., 2020; Wahid et al., 2023). It is also considered an influencer-mediated model of communication, focused on building social influence (Varadarajan et al., 2022). The rise of other types of content on the platform has encouraged the entry of companies into the platform, and which claim to obtain results from its use from a marketing perspective (TikTok, 2021). From a marketing perspective, some studies have been conducted to analyse the impact of content strategies on the web. Wahid et al. (2023) analyse how characteristics such as the informative or emotional nature of the message and the use of verbal and nonverbal language influence user engagement. Barta et al. (2023) study the determinants of success in terms of originality, quality, quantity and the use of humour, while other research focuses on content analysis in specific sectors, such as media outlets (Mudra and Kitsa, 2022), sports (Su et al., 2020) or luxury (Castillo-Abdul et al., 2022). To investigate the determinants of the strategic entry decision in TikTok, this study uses a sequential mixed-methods approach, employing both qualitative and quantitative data collection and analysis techniques (Vivek and Nanthagopan, 2021). Specifically, we apply a combination that connects the data in three phases: an exploratory design, which follows a sequential distribution from a qualitative to a quantitative analysis (phases 1 and 2) and an explanatory design, which in this case follows an opposite sequential distribution, moving from the preceding quantitative analysis to qualitative analysis (phases 2 and 3). The use of a sequential mixed-methods approach is justified for several reasons. The exploratory design is suitable for this study as it allows for the sequential examination of qualitative data through a focus group followed by quantitative analysis based on data collected through questionnaire. This enables us to determine the relevance and applicability of the theoretical foundations of this research, particularly the variables derived from the UTAUT model and marketing agility, within the specific context of the study. It is essential to understand whether these variables are determinant in the real business context and to understand whether they make sense when analysed in conjunction, since we consider that they are two concepts that converge but that have not been examined together before. Moreover, this analysis facilitates a contextual understanding of the phenomenon and substantiates its relevance at the business level, enhancing the robustness of the results. Following the completion of the qualitative study and the confirmation of the variables’ interest and relevance, the quantitative analysis is performed with confidence in the appropriateness of the selected variables. This quantitative analysis is based on a logistic regression carried out on the data obtained from a questionnaire distributed to 161 brand social network managers. Subsequently, the explanatory design builds upon the previous quantitative analysis, as the subsequent qualitative analysis employing Fuzzy-set Qualitative Comparative Analysis (fsQCA) provides a more concrete, comprehensive, and detailed explanation of the results obtained in the quantitative analysis. 4. Study 1: Qualitative research Study 1 was conducted in a real context through the use of a focus group. The primary aim of this initial study was to identify the significant factors that influence the strategic decision-making process of I. Oltra et al. Journal of Business Research 187 (2025) 115054 4 companies when entering social networks. Specifically, the study aimed to investigate the potential complementarity between variables related to marketing agility and the UTAUT innovation adoption model. By doing so, the study sought to determine the relevance and applicability of these variables in the context of real-world decisions made by companies when considering entry into social networks. The secondary objective of Study 1 was to draw conclusions that serve to outline the hypotheses of our research, which will subsequently be argued in Study 2. 4.1. Methodology Study 1 adopts a qualitative research approach employing the focus group technique (Wilkinson, 1998). This study seeks to analyse the perspectives of social network managers regarding the decision to enter social networks –particularly for brands that are already substantially and actively present on these platforms– by means of organic firm generated content, i.e., strategies that do not include paid advertising content, such as ads or influencer campaigns. Selecting the focus group method over individual interviews is justified by the desire to generate a specialized discussion on the topic and to delve deeply into industryrelated issues and current affairs which –individually in a conversation between interviewer and respondent– may not appear. In other words, the active listening of other similar professionals and the possibility of intervening to emphasize issues discussed or to highlight different viewpoints benefits the development of the topics to be investigated (Thomas et al., 2004). Moreover, bearing in mind that this is an exploratory phase of the research, the use of a focus group is appropriate as it enables a preliminary understanding of the phenomenon prior to conducting more conclusive analyses. The focus group was conducted face-to-face in November 2022. It was recorded and subsequently transcribed, engaging in a conversation that lasted one hour and 52 min. It involved the participation of eight marketing and social media executives, representing companies in the Spanish market. The selected company profiles represented diverse sectors, including energy, sports, retailing, media, culture, and Fintech. Additionally, two professionals from digital marketing agencies were included as part of the focus group. Inclusion of these profiles allows for a comparison between internal and external management of social networks, which later serves as a control variable in the quantitative model. Selection of these profiles was based on criteria of both homogeneity and heterogeneity. Homogeneity was ensured regarding their work positions within their respective companies, as all participants held strategic roles in social network management and digital marketing, making them experienced decision-makers in the topics discussed. Moreover, homogeneity criteria were applied to their level of brand presence on social networks –which had to be very high. In turn, heterogeneity criteria were applied in terms of the sectors in which their companies operated and their years of professional experience in the field so as to achieve a comparison of the different possible perspectives according to these variables. Table 2 provides a description of participants’ profiles. The focus group session followed a structured format, dividing the topics into three distinct groups: the current social network strategy employed by their respective companies, the strategies employed when entering new social networks (with a specific focus on TikTok), and the factors influencing the decision-making process for entering new networks (Table 3). The script was designed without including the UTAUT and marketing agility variables as part of it in order to verify whether these concepts really emerged spontaneously. 4.2. Data analysis Data analysis was supported by thematic analysis. After a thorough reading of the transcript, codes were identified and from these, themes and sub-themes were established –following Braun and Clarke (2006). A brief summary of these can be seen in Table 4. The insights obtained from the focus group conversation are presented below and illustrate how the different themes and sub-themes emerged during the conversation. Participants in the focus group acknowledged that the decision to enter TikTok was a challenge that all their brands had encountered. Interestingly, all the profiles represented in the discussion have decided to enter TikTok with their brands and are currently on TikTok, except for the two participants representing agencies. They recognize that not all the brands they work with have agreed to start a strategy on this new social network. The limited availability of resources and the difficulty for some clients in visualising the possible results of developing a strategy on TikTok seem to be the main reasons for this decision. The constraints imposed by resource limitations led to a need for efficiency Table 2 Description of participants. Brand Information Brand Information Iberdrola Sector. Supply of electric power, gas, steam and air conditioning. Market. International (Spain, United Kingdom, USA, Brazil, and Mexico) Social media size. 400 K Position. Digital & Social Media Director Experience. 27 years Verse Sector. Fintech Market. International (Europe) Social media size. 1.5 M Position. Head of Social Media and Influencers Experience. Seven years El Corte Ingl´ es Sector. Wholesale and retail trade Market. International Social media size. 3.4 M Position. Digital Marketing Director Experience. 19 years SocialMood Sector. Digital marketing agencyClient (only for agencies). Ron Barcel´ o Market. National (Spain) Position. Head of creative strategy Experience. 21 years Museo del Prado Sector. Museum Position. Head of digital communication Market. National (Spain) Social media size. 5 M Experience. 17 years BrandCrops Sector. Digital marketing agencyClient (only for agencies). Legado Ib´ erico Market. National (Spain) Position. Chief Executive Officer Experience. 10 years Movistar Team Sector. Sports Market. National (Spain) Social media size. 2 M Position. Head of communication Experience. 15 years C´ odigo Nuevo Sector. Digital media Market. National (Spain) Social media size. 1.1 M Position. Social Media Manager Experience. Eight years *Social media size: total number of followers on Instagram, Facebook, TikTok and X (formerly Twitter). *Client: most named client by the agency during the focus group. Table 3 Focus group script. 1. Short presentation Who are you: education and work experience, company and business situation. 2. Current strategy Social media accounts, intensity of the strategy in each network, importance of each network, form of management: external / internal, form of organization and roles. 3. Entry strategy Process for assessing entry into a social network. Degree of presence in TikTok and strategy in the network: objective and importance. Entry decision: how and when. Expected / obtained results. Impact for other networks (substitution effect / synergies). Overview in relation to other networks. I. Oltra et al. Journal of Business Research 187 (2025) 115054 5 in decision-making. Consequently, brands allocated their resources to networks where they could achieve a quicker return –particularly those platforms where strategies were already implemented and did not require a significant initial launch effort. “There is reluctance on the client’s part, a difficulty in understanding what needs to be done or embracing a new mindset in some way. And there is also a resource issue; you have to allocate resources to all the networks, and there is an ever-increasing number of networks. It’s time and money that they often aren’t willing to invest.” The issue of resource availability sparks a significant debate regarding entry into TikTok. There is unanimous agreement on the significant time investment required to develop a new strategy. The accounts managed by the participants in the study boast hundreds of thousands of followers, creating an impression of having large marketing teams supporting them. However, this is not always the reality, as small teams often take on the challenge of entering new networks without any increase in available resources. In this context, participants indirectly allude to the notions of effort and performance expectations outlined in the UTAUT model at the beginning of the discussion. The most notable advantage in this context is that all networks are currently trending towards a common content format: short videos. While this presents an opportunity by enabling the creation of content that can be shared across multiple networks, it also poses a challenge. Social network managers must adapt to this new format, and it will require them to acquire new skills and knowledge. In addition to learning how to create content, they must understand the inner workings of the new social network, including its algorithms and operational mechanisms. This learning process involves a significant learning curve as they familiarize themselves with the intricacies of the platform. “We had to learn to use a social network not as a user, but as a professional, and that means you have to have a deep understanding of the algorithms. You need to learn, you have to start trying things out and see what works.” Contradictions do, nevertheless, arise: some participants highlighted the perceived difficulty of starting from scratch and acquiring new skills, while others emphasized the ease of content creation and the potential for a trial and error approach. They argued that the content demanded by the platform does not require excessive effort on the part of the brands to be published. This ease of content creation and its crossplatform applicability align with the notion of facilitating conditions, as described in the UTAUT model. “At the effort level, it’s challenging for us, but well, we’re trying.” “One of the things that made us get into TikTok is that homemade content is rewarded. You don’t have to edit much, or you can edit quickly, and then the return is tremendous.” At this stage, the conversation focused on agility. The profiles of the participants represent various companies with distinct sectors, sizes, and internal organizational processes. Consequently, while some participants acknowledged their agility in execution, others expressed concerns about their lack of agility in decision-making and implementation. Company size emerges as a determining variable in these observed differences and perspectives. “What we lack most is agility, which smaller brands can have.” All participants agreed that considering the market is essential when making entry decisions. They considered the importance of listening to the market and of understanding trends in order to determine the optimal timing and strategy for entry, and even to anticipate them. They identified the essential need to be aware of real time, of what is happening and changing at all times in the audience and the market. “Before doing anything at all, it’s important to have a deep strategic approach and to try and understand your audience. Because the audience evolves and changes, obviously.” The team’s role was also highlighted, as diverse perspectives and ideas from their members contribute to the development and implementation of effective strategies. In the case of TikTok, they recognized the moment when Generation Z began migrating to the platform as a pivotal point for reflection and for considering entry; in short, not doing things without thinking and reflecting on whether their brand has a place at that moment in the social network. These insights align with the concept of sensemaking: that is, a shared understanding of the market based on the market listening to anticipate trends and on the role of teamwork. During the conversation, the concepts of speed and flexibility –which are core to marketing agility– also emerged organically. Participants agreed that continuously listening to the audience and the market is not only necessary for initial decision-making but also for ongoing adjustments and adaptations. The strategies developed must have structured thinking behind them yet must also allow for constant adaptation. Furthermore, speed is defined as a key element to face all the changes detected so as to effectively implement the answers that need to be given from a strategic level. The need was also mentioned for a solid construction of the brand that allows quick executions without compromising brand coherence. “From the very beginning, there was a thinking behind it, an initial plan. Then over time, as the social network grew, we grew along with it and adapted our strategies.” And what is the objective to be achieved by entering? Brands recognize that at the current time TikTok is not ready to help companies improve their business conversions (in terms of web traffic and sales). Instead, their performance expectations are focused on the long term. By entering TikTok now, these brands aim to establish a presence and to build a brand strategy that positions them for future success when the platform evolves and becomes more conducive to conversions. Consequently, some of these brands acknowledge that they continue to allocate more resources to other networks such as Instagram or Pinterest because they provide them with results in the short term. TikTok, in their view, is approached as a strategic investment for future Table 4 Overview of concepts. Theme Sub-themes Codes Quotes Marketing agility Sensemaking Real time Changes Environment Alert Listen “The audience evolves and changes, obviously.” “You have to be aware of real time.” Speed Quickly Reaction Time Be the first “We take into account the speed to adapt quickly. We have to react.” Flexibility Adapt Flex Adjustment “We also don’t rule out changing our strategy a while from now, of course. We always adapt”. UTAUT Effort expectancy Know Professional Learn “We had to learn how to use a social network not as a user, but as a professional. And that implies that you have to have a great deal of knowledge.” Performance expectancy Results Failure Conversion “A lot of people say TikTok isn’t right for conversion, for now.” Facilitating conditions Easy Preparation Content creation “You don’t have to edit a lot, or you can edit fast, and then the return is unbelievable.” Social influence Competitor Society Influence “You’re going to be influenced to decide to go in. Sometimes it’s the push from competitors, and sometimes it’s the push from society.” I. Oltra et al. Journal of Business Research 187 (2025) 115054 6 opportunities rather than as an immediate source of tangible outcomes. “The day TikTok generates the sales results of Instagram, then my strategy will change radically. But for now, I sell a lot on Instagram because ultimately what I want is to make sales. So, I’ll stick with Instagram.” Finally, the influence of external factors on the decision to enter TikTok beyond the company itself, its operations and expectations, was discussed. There was consensus: both competition and society can spur the decision to enter. The concept of social influence –as presented in the UTAUT model– resonated with their reflections. In the context of social networks, competition extends beyond companies within the same industry targeting the same audience. Competition now encompasses any alternative content available on the platform. In the case of agencies, participants acknowledged that some clients –after observing their competitors– express a desire to enter TikTok and exert pressure on the agencies to do the same. The role of the agency in these cases is to act conscientiously and not to succumb to a request if they lack clear objectives. The same situation arises for brands that manage their networks internally. When they see that others are entering and taking action, it serves as a catalyst for them to act promptly to avoid falling behind. “In our case, it wasn’t so much due to competitor pressure, but because we saw that people were coming and that we had an audience.” “This brand has entered; this brand has more ideas. It’s inevitable to think about it; it’s pure psychology.” “When we talk about benchmarking, I believe it’s good to look at what others are doing, but what’s even better is to understand why they are doing it.” This qualitative analysis allowed us to gain valuable insights into the research objective and to design the quantitative study. First, the focus group revealed the presence of the main variables of the UTAUT model (Venkatesh et al., 2003). Participants mentioned the influence of expected effort and performance, previous learning, and the behaviour of other brands. Moreover, they recognized that entry may reflect shortterm and long-term performance expectations. We therefore include in our proposal the following variables: effort expectancy, short-term performance expectancy, long-term performance expectancy, facilitating conditions, and social influence. Second, in terms of marketing agility, participants referred to speed, flexibility, and sensemaking, while responsiveness was not explicitly mentioned, possibly due to its close association with the other concepts. Indeed, reacting, deciding, and responding quickly to changes are aspects that are implicit to speed and flexibility. As a result of this, and considering the emerging nature of the concept –with multiple studies in other areas that also decided to include some components rather than others in terms of agility (e.g. Chuah et al., 2020; Gligor and Bozkurt, 2021)– we decided to establish our definition of marketing agility based on sensemaking, speed, and flexibility. We also observed that sensemaking implies monitoring and anticipating trends, but also teamwork to approach decisions from diverse perspectives. This underscores the importance of studying both aspects and of determining their relative importance in predicting brand behaviour when making entry decisions in a social network. 5. Study 2a: Quantitative analysis Based on the study variables defined and the findings from Study 1, the second study sought to further investigate and quantify the results obtained. While Study 1 employed a focus group as an initial exploration of strategic decision-making for brand entry into new social networks, Study 2 applied a quantitative approach through questionnaire data to examine the factors identified (UTAUT and marketing agility), both within the study itself and in its theoretical framework, thus providing a more rigorous analysis. This quantitative approach was designed to enhance our understanding of the factors identified and their impact on the decision-making process, thereby offering a deeper level of analysis and interpretation. 5.1. Hypotheses development Following previous literature and the results to emerge from the focus group, we opted to use the UTAUT model and the concept of marketing agility to justify our research hypotheses. According to the UTAUT model, adoption intention and subsequent adoption of a technology can be explained through four determinants (Venkatesh et al., 2003). Notably, performance expectancy has been found to have a significant positive impact on the adoption intention of social networks (Tajudeen et al., 2018), particularly in small businesses (Humaid and Ibrahim, 2019). Compared to the rest of the variables in the UTAUT model, these expectations have the strongest influence on this adoption intention (Puriwat and Tripopsakul, 2021). Zhou and Matsaganis (2020) reach the same conclusion: effort expectancy is a higher-level construct than the rest of the variables in the model. To effectively promote adoption, decision-makers need to demonstrate the real utility of the network. During the focus group discussion, a distinction was made between short-term and long-term performance expectations. While the literature generally acknowledges their overall influence, industry professionals contend that when a new network emerges, said professionals must carefully evaluate its potential results in the immediate moment, which is characterized by novelty and uncertainty. Simultaneously, they consider the long-term future with greater stability and expectations. Based on these insights, we propose the following hypothesis: H1: Performance expectancy in the short-term (H1a) and in the longterm (H1b) positively influence a company’s decision to enter a new social network. Similarly, the focus group discussion highlighted the significance of effort expectancy as a crucial variable in the decision-making process of adopting a new network, specifically emphasizing the need to acquire the necessary knowledge and skills before implementing a strategy. Previous literature supports this notion, with Mandal and McQueen (2012) finding that despite recognizing the usefulness and potential of networks such as Facebook, considerable effort is required to create engaging content compared to other advertising channels such as radio or outdoor advertising. Puriwat and Tripopsakul (2021) rank performance expectancy as the second most influential factor and assert that social networks are the digital marketing tool that requires the least effort for companies to adopt. Taking these insights into account, we propose the following hypothesis: H2: Effort expectancy positively influences a company’s decision to enter a new social network. Facilitating conditions are recognized as another key element in companies’ intention to use social networks. Organizations that perceive their structure as being more prepared show higher adoption intentions (Zhou and Matsaganis, 2020). This viewpoint aligns with the insights shared by the professionals in our focus group, who emphasized the importance of capabilities and the time required for content creation, including ideation, editing, and publishing. Humaid and Ibrahim (2019) similarly find a positive influence of facilitating conditions in terms of physical resources, knowledge and technical support on the intention to adopt social networks. We thus propose the following hypothesis: H3: The existence of facilitating conditions positively influences a company’s decision to enter a new social network. The last element, social influence, which is also a classic factor of the UTAUT model, appears to have different effects in the context of social networks. Some studies, such as Mandal and McQueen (2012), Vatanasakdakul et al. (2020) and Zhou and Matsaganis (2020) find that social influence is not a significant factor in the overall adoption intention of social networks. They define social influence as the perception that I. Oltra et al. Journal of Business Research 187 (2025) 115054 7 others believe the company should be using social networks. In contrast –and in line with the classical results of this variable applied to other areas outside social networks– Humaid and Ibrahim (2019) and Tajudeen et al. (2018) find that social influence (including influence from competitors, customers, friends, and family) has a major impact on a company’s intention to use social networks. The focus group also hints at this possibility of influence –mentioning society and competition as possible drivers of entry decisions. Although previous literature states that the decision to adopt social networks (the decision to start using social networks) is not influenced by the social component, the latter does possibly play a key role in the specific decision concerning whether or not to enter a new network; in other words, deciding to add another social network to the general strategy. Therefore, we propose the following hypothesis to test this potential influence: H4: Social influence positively influences a company’s decision to enter a new social network. The concept of marketing agility has not been applied in academic research to studies on the adoption of social networks but has only been used as a construct to analyse the execution of established network strategies. This means that in the case of this construct, the focus group acquires special relevance in deciding to include a related hypothesis. Its key components –sensemaking, speed and flexibility (Kalaignanam et al., 2021)– were repeatedly mentioned. Sensemaking emerged as the need to closely observe audiences and market conditions, which played a crucial role in the decision-making process of entering a network. Sensemaking also implies considering multiple perspectives when monitoring the market and making decisions. Brands that have marketing teams who listen to the market and who bring together different ideas and perspectives in order to stay on top of trends will steal a march on other brands when entering new social networks. Additionally, flexibility and speed were highlighted as essential qualities for adapting to changes and making timely decisions, even anticipating market trends. Due to the continuous changes that characterize the social media environment, entering a new social network means that companies must be able to adapt to change and, if necessary, quickly implement new activities and proposals for their audience. We therefore propose the following hypothesis: H5: Marketing agility –in terms of sensemaking (H5a), speed (H5b), and flexibility (H5c)– positively influences a company’s decision to enter a new social network. The proposed hypotheses are represented graphically in Fig. 2. 5.2. Sample and data collection Data collection for this study took place through an online questionnaire distributed between December 2022 and February 2023. Given the novelty and trend of the phenomenon of brand entry in TikTok, we verified that there were no significant differences between the responses of the first and last respondents to the questionnaire ( χ 2 (1) = 0.898 (0.343)). Prior to its distribution, a pre-test of the questionnaire was conducted with three volunteer social network managers. The aim of the pre-test was to identify any possible misunderstandings related to the items. Certain items were seen to exhibit duplicity in meaning, leading to their subsequent removal from the questionnaire. The target audience were social media department managers in companies with a well-established and structured presence in the Spanish market’s social networks –achieved through organic means. Study participant selection was carried out using non-probabilistic judgmental sampling. Selection followed strict relevance criteria in order to control the characteristics thereof. First, a list of companies with a strong presence in social networks in the Spanish market was established. This list was ordered by general brand recognition in the market. Once the list had been established, we identified and contacted the specific person responsible for the company’s strategic decisions in social networks. Specifically, the LinkedIn social network was used to identify individuals holding the desired management positions, and they were subsequently contacted via private messages on LinkedIn and email. A total of 161 completed questionnaires were finally obtained for analysis. All the questionnaires were completed by the chief marketing officers or social media managers of the companies. The sample for this study was carefully composed to ensure a diverse range of perspectives within the model. The participating companies represent various sectors, including food, beverages, health, insurance, banking, automotive, fashion, leisure, e-commerce, pharmaceuticals, mobility, NGOs, real estate, telecommunications, tourism, restaurants, education, energy, cosmetics, and sports. As for their marketing teams, the average size is 11 employees (ranging from 1 to 150) and the average number of people dedicated exclusively to social media management is four employees. Among those who claimed to be registered on the network (71 %), 10.6 % registered before 2020, 17.4 % in 2020, 18.6 % in 2021, and 24.8 % in 2022. These figures are worthy of note in comparison to when constant publication first commenced on the network, where only 4.3 % did so before 2020, 9.9 % since 2020, 19.9 % since 2021 and 37.3 % since 2022. Fig. 2. Proposed research model. I. Oltra et al. Journal of Business Research 187 (2025) 115054 8 5.3. Measures and validity checks The questionnaire first asked all subjects for the degree of agreement with a series of possible factors influencing the overall strategic decision-making process in social networks in terms of marketing agility. We proposed a scale of 12 items to measure marketing agility that were extracted and adapted from the measurement of sensemaking proposed by Mu et al. (2018) and Neill, McKee, and Rose (2007), the measurement of speed by Lu and Ramamurthy (2011), and the scale of flexibility proposed by Khan (2020). In a second stage, participants were asked about the factors related to the UTAUT model. Scales measuring the variables of the UTAUT model are adaptations to our study context of the scales by Venkatesh et al. (2003) to measure effort expectancy, performance expectancy, and facilitating conditions. Items concerning social influence were adapted from Stibe and Cugelman’s (2019) scale. For these variables –and given that we found companies which had entered TikTok and others which had not– we needed to adapt the verb tense of some of the items to reflect the current situation of the company’s incorporation of TikTok. We used present and past tenses for companies which had entered, and future or conditional tenses for those which had not (e.g., “It is useful in the short term” vs. “It may be useful in the short term”). As control variables, subjects were asked about the number of employees in the company (48 % reported less than 50 employees, 52 % more than 50), the number of followers in social networks (44 % reported less than 100,000; 56 % more than 100,000), the form of management of their social networks (17 % externally through an agency, 83 % internally), the team’s social media experience (seven-point Likert scale), and the team’s exclusive dedication to social media (seven-point Likert scale). We analysed the relationship between the control variables and found that the number of followers was positively related to the team’s social media experience and to its exclusive dedication to social media. When an account acquires more followers, greater team dedication and expertise is likely to be required. Having more specialised and dedicated staff may even help the accounts to grow. As a result, we considered the number of followers as a proxy variable for team experience and team exclusivity and we did not incorporate them into the regression model. The dependent variable “entry on TikTok” was reflected through the question “Is your brand on TikTok?”, the response options for which were “No”, “Yes, only with an advertising account”, “Yes, only with an organic account” and “Yes, with an advertising and organic account”. In addition to obtaining the response of the dependent variable yes/no, the possible information bias caused by brands using TikTok with a Social Ads advertising account but without organic content was thus avoided. This variable was recoded to obtain the binary variable with unique yes/ no values (29 % no, 71 % yes), removing in the “yes” option those who were only present with an advertising account. In order to validate the dimensions of marketing agility, we first conducted an exploratory factor analysis (EFA) with principal axis factoring to verify that the items referring to each dimension were grouped as proposed in the measurement scales. EFA (Kaiser-Meyer Olkin (KMO) measure of sampling adequacy =0.786 and Bartlett’s test of sphericity sig. =0.000) revealed four factors that reflected speed, flexibility, and two dimensions of sensemaking: the capability to anticipate new trends −sensemaking advance −and teams able to integrate different perspectives and points of view −sensemaking team. This result confirms the two aspects that cover sensemaking and that were already manifested in the focus group. We then conducted a confirmatory factor analysis (CFA) with these four dimensions as first-order constructs, which indicated an acceptable goodness of fit ( χ 2 (21) =30.42 (p =0.084), GFI =0.96, AGFI =0.914; CFI =0.974; RMSEA =0.053). As for the UTAUT variables, we also conducted an EFA (KMO =0.781; Bartlett’s test sig. =0.000) that yielded a five-factor solution reflecting the five constructs. We thus performed a CFA using AMOS to validate the five scales, with results showing an adequate goodness of fit ( χ 2 (79) =132.25 (p =0.000), GFI =0.906, AGFI =0.857; CFI =0.956; RMSEA =0.065). Table 5 shows the items in the study, the descriptive statistics and the loadings. We assessed the scales’ reliability and verified that composite Table 5 Constructs and measures. Mean SD CFA loadings Sensemaking-advance (CR:0.825; AVE:0.704)    We continuously monitor information on new trends in social networks. 6.12 1.027 0.779 We anticipate social media trends before they are fully evident. 4.37 1.461 0.895 Sensemaking-team (CR:0.797; AVE:0.668)    We consider all possible perspectives when making decisions about social media trends. 5.16 1.533 0.930 We make decisions using different points of view from all team members. 5.07 1.791 0.686 Speed (CR:0.861; AVE:0.676)    We reduce as much as possible the time between decision-making and its implementation in our social media strategy. 4.99 1.537 0.908 We are quick to make decisions based on market or user changes. 4.98 1.539 0.862 We quickly change decisions that do not produce the expected results. 5.03 1.575 0.680 Flexibility (CR:0.864; AVE:0.763)    We are flexible in dealing with changes that arise and that may affect our strategy. 5.52 1.295 0.788 When unexpected situations arise, we work to make adjustments or changes rather than remain static. 6.14 1.012 0.951 Short-term performance expectancy (CR:0.894; AVE:0.681)    It is useful in the short term for the company. 4.43 1.964 0.798 It improves our performance (results) in social networks in the short term. 4.69 1.877 0.903 It helps to improve our business results at the conversion level (considering conversion as the key business metric, be it sales, turnover, traffic, etc.) in the short term. 3.72 1.871 0.731 It helps to maintain and/or improve our brand positioning and branding in the short term. 5.45 1.642 0.858 Long-term performance expectancy (CR:0.930; AVE:0.770)    I think it will be useful in the long term for the company. 5.92 1.346 0.948 I think it will improve our performance (results) in social networks in the long term. 5.80 1.396 0.904 I think it will help to improve our business results at the conversion level. (considering conversion as the key business metric, be it sales, turnover, traffic, etc.) in the long term. 5.27 1.634 0.793 I think it will help to maintain and/or improve our brand positioning and branding in the long term. 5.98 1.206 0.859 Facilitating conditions (CR:0.903; AVE:0.758)    We have enough employees to incorporate TikTok into our social media strategy. 3.37 2.002 0.914 We have enough time to incorporate TikTok into our social media strategy. 3.39 1.778 0.962 We have enough knowledge to use TikTok in our social media strategy. 4.88 1.672 0.717 Effort expectancy (CR:0.852; AVE:0.742)    How TikTok works (from a branding point of view) is easy to understand. 4.57 1.731 0.815 Learning how to create professional-level content for your brand on TikTok is easy. 3.65 1.732 0.906 Social influence (CR:0.800; AVE:0.678)    Before joining TikTok, we learned by observing other brands before incorporating TikTok into our strategy. 5.00 1.809 0.981 To decide to join TikTok, we compared ourselves to other brands. 4.25 2.000 0.627 * α presents values lower than 0.7 in four of the constructs. 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