What drives individuals’ trusting intention in digital platforms? An exploratory meta-analysis

Oesterreich, Thuy Duong; Anton, Eduard; Hettler, Fabia Marie; Teuteberg, Frank Article — Published Version What drives individuals’ trusting intention in digital platforms? An exploratory meta-analysis Management Review Quarterly Suggested Citation: Oesterreich, Thuy Duong; Anton, Eduard; Hettler, Fabia Marie; Teuteberg, Frank (2024) : What drives individuals’ trusting intention in digital platforms? An exploratory metaanalysis, Management Review Quarterly, ISSN 2198-1639, Springer International Publishing, Cham, Vol. 75, Iss. 4, pp. 3615-3667, https://doi.org/10.1007/s11301-024-00477-2 This Version is available at: https://hdl.handle.net/10419/333225 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. 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Anexploratory meta‑analysis ThuyDuongOesterreich1 · EduardAnton1· FabiaMarieHettler1· FrankTeuteberg1 Received: 6 March 2024 / Accepted: 1 November 2024 / Published online: 19 November 2024 © The Author(s) 2024 Abstract The primary objective of this study is to delve into the determinants influencing individuals’ intention to trust digital platforms. Therefore, we conduct a meta-anal- ysis of 74 primary studies that investigate the role of trusting beliefs, technology acceptance factors, as well as variables associated with systems, users, and platform providers in driving trust. We found evidence that all trust antecedents positively impact trusting intention in digital platforms. Notably, human-like trusting beliefs, attitude, platform provider image/reputation, structural assurance, perceived enjoyment, and perceived usefulness display the strongest effects, while familiarity, perceived value, and system-like trusting beliefs exhibit a moderate yet significant influence. Additionally, publication year was found to significantly moderate the relationship between service quality and trusting intention, indicating a temporal effect on the outcomes. Through these findings, we contribute to the body of knowledge by guiding scholars across research disciplines towards future research avenues and aiding practitioners in the development of trustworthy digital platforms. Keywords Digital platforms· e-commerce· Technology trust· Meta-analysis· Moderator analysis JEL Classification M15· O30· O32· O33 * Thuy Duong Oesterreich thuyduong.oester[email protected] Eduard Anton [email protected] Fabia Marie Hettler [email protected] Frank Teuteberg [email protected] 1 Department ofAccounting andInformation Systems, Osnabrück University, Osnabrück, Germany 3616 T.D.Oesterreich et al. 1 Introduction In the past few decades, the rise of the internet, along with the proliferation of internet-capable mobile devices, has triggered an accelerated expansion of e-commerce in general and digital platforms in particular (Akroush and Al-Debei 2015; Limbu etal. 2012). Between 2015 and 2022, the number of internet users increased by 65% from 3.0 to 4.95 million and the number of smartphone users increased by 46% from 3.6 to 5.3 million, while the growth in the global population was comparatively moderate at only 11% (We Are Social Inc., 2015, 2022). Current statistics regarding weekly online purchases in 2022 reveal that a notable58.4% of working-age internet users (aged 16 to 64) engage in online shopping on a weekly basis. This percentage is expected to continue its upward trajectory in the coming years (We Are Social Inc., 2022), underscoring the substantial economic potential inherent within this domain. Along with these trends, e-com- merce platforms like Amazon and eBay, as well as sharing platforms like Airbnb and Uber, have contributed to the rapid evolution of digital platforms, leading to an intensified competition landscape (Akroush and Al-Debei 2015; de Reuver etal. 2018; Ke etal. 2016). While physical retail settings allow trust to be built on tangible, face-to-face interactions, digital platforms present a unique challenge. Buyers often find themselves engaging with sellers or entities with whom they have no prior familiarity (Kim etal. 2008; Oliveira etal. 2017; Pavlou and Gefen 2004). In such a digital landscape, trust becomes a paramount concern, given that this absence of prior rapport introduces heightened perceived risks (Kim etal. 2008; Sutherland and Jarrahi 2018). For example, security concerns regarding online payments, company reliability, and inadequate data privacy policies are among the most important factors that influence consumer trust (Gefen, 2000). Despite these challenges, the foundation of any successful digital transaction remains rooted in trust. Only with a deep-seated trust in a platform are users willing to share personal information and engage in transactions. This underlines how trust is not just crucial, but indispensable for shaping purchase intentions and ensuring the wider acceptance of digital platforms (Räisänen etal. 2021; Sutherland and Jarrahi 2018). Due to the significant role of trust as a predictor of IT usage (Mcknight etal. 2011; Söllner et al. 2016), an increasing number of studies have delved into trust-related topics. These studies encompass various aspects of users’ trust in digital platforms, spanning online shops like Amazon (Gefen and Heart 2006; Gefen and Straub 2004; Pavlou 2001a), online auction marketplaces such as eBay (Liu and Tang 2018; Sun and Zhang 2008; Tu etal. 2012), and sharing platforms like Airbnb (Mao etal. 2020; Wang etal. 2020). While this extensive body of trust-related research has undoubtedly enriched our understanding by offering valuable insights into distinct platform contexts, recent studies have underscored the necessity for a more comprehensive accumulation of knowledge pertaining to digital platforms (Sutherland and Jarrahi 2018), particularly in the realm of trust-building mechanisms (Räisänen etal. 2021). Moreover, scholars across research fields have highlighted the need to deepen our understanding of 3617 What drives individuals’ trusting intention indigital… platform dynamics, as current studies often offer a static snapshot of platforms. Studying the dynamics of digital platforms within an extended time frame can reveal changes over time, thus emphasizing the importance of longitudinal studies to understand the evolving nature of digital platforms (de Reuver etal. 2018). However, to date only a limited number of research endeavors have been dedicated to consolidating knowledge regarding trust-building factors within the digital platform context based on empirical findings from prior studies. Gaining a comprehensive and coherent understanding of the complete spectrum of factors influencing trust in digital platforms would help both scholars and practitioners in refocusing their attention on less explored factors that require deeper investigation. In addition, cumulative knowledge regarding the most important trust-building factors within the digital platform context would serve to assist both scholars and practitioners with the development and design of digital platforms in the future, leading to enhanced user experiences through the delivery of trustworthy and better designed digital platforms. Prior meta-analyses that focused on IT-related trust have predominantly centered on trust relationships within broader contexts such as electronic commerce (He 2011; Kim and Peterson 2017), mobile commerce (Sarkar etal. 2020; Zhang etal. 2012), and various other electronic services (Mou etal. 2017; Zhao etal. 2018). However, these analyses have not comprehensively addressed the specific context of trust relationships between users and digital platforms. In order to bridge this gap, our study seeks to address the following research questions (RQs): RQ1: What are the main factors that drive individuals’ trusting intention in digital platforms? RQ2: To what extent do moderating factors cause these trust-building factors to have varying impacts on individuals’ trusting intention in digital platforms? To answer these research questions, we conduct a meta-analysis that quantitatively synthesizes the empirical findings reported in 74 studies from 72 journal articles and conference papers. We view meta-analysis as a suitable research approach for consolidating findings from divergent studies to reconcile disparities (Hwang 1996) and enhance the statistical robustness of the outcomes (Hunter and Schmidt 2004, p. 75). We draw on the eight-step meta-analysis approach outlined by Hansen etal. (2022) for guiding our meta-analysis paper. Section 2 of our paper delves into the theoretical background on technologyrelated trust research and provides an overview of related works. Subsequently, Sect.3 presents the research framework, including the structural and moderating variables of interest. Section4 provides details on our multi-step research approach of our meta-analysis, encompassing the literature search and selection, documentation and coding, as well as the data analysis steps. The meta-analytic results are presented in Sect.5, including the findings from our robustness tests and moderator analyses. The implications for research and practices, as well as the limitations of our study are presented in Sect.6. Finally, concluding remarks are provided in Sect.7. 3618 T.D.Oesterreich et al. 2 Trust indigital platforms The ongoing digital transformation within the economy and society has underscored the increasing importance of trust-related concerns in IT across a multitude of application contexts (Söllner etal. 2016; Vance etal. 2008). Trust becomes particularly significant in the context of high-risk IT usage (Borsci etal. 2019). For instance, when engaging in e-commerce transactions, individuals must place their trust in the technology to mitigate the potential risks of financial and privacy losses (Friedman etal. 2000; Mou etal. 2017). In the context of digital platforms, trust unfolds through three distinct dimensions: (1) Users’ trust in the platform operator, (2) Trust in the technology behind the platform, and (3) Trust in the interaction among users (Räisänen etal. 2021; Sutherland and Jarrahi 2018). Consequently, trust-building factors are likely to manifest through particular platform, provider, and user attributes that align with these three spheres. This multi-faceted nature of digital platforms becomes evident when considering the question of how to define a digital platform. In this context, the Federation of German Industries (BDI 2022) defines digital platforms as “intermediaries using digital technology to connect two or more market participants via the platform and simplify or even enable their interaction”, while de Reuver etal. (2018, p. 127) proposes a sociotechnical view of digital platforms as “technical elements (of software and hardware) and associated organisational processes and standards” Sidorenko (2022) additionally pointed to the legal characteristics of a digital platform which encompass its technological infrastructure facilitating interactions between producers and consumers, the content of its core services, and its distinct role as a digital intermediary connecting buyers and sellers of goods and services. Today, digital platforms have become an integral part of our daily lives, bearing the potential to change entire industries (de Reuver etal. 2018; Hein etal. 2020). Online marketplaces like Amazon and eBay offer convenient access to a wide range of products and services at a glance, whereas sharing platforms like Uber and Airbnb have expanded the availability of services to a diverse array of people. Likewise, payment platforms like PayPal and Apple Pay are transforming the financial industry (Fu etal. 2021). Given the integration of digital platforms into our daily lives and their substantial impact on consumers across diverse dimensions (de Reuver etal. 2018), the matter of trust in these platforms naturally comes to the forefront. In alignment with previous literature on technology-related trust (Gefen and Pavlou 2011; Kim etal. 2008; Mcknight etal. 2011), we embrace the integrative model of organizational trust (Mayer etal. 1995) as the foundation for conceptualizing trust within the realm of digital platforms. According to Mayer etal. (1995), trust can be defined as “the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party”. Given our focus on trust in digital platforms which, as stated above, encompasses a major technological component, we additionally refer to the human-technology-oriented perspective on trust as proposed by Lankton etal. (2015, p. 883) and McKnight etal. (2011) to examine “trusting 3619 What drives individuals’ trusting intention indigital… beliefs in technology, which are beliefs that a specific technology has the attributes necessary to perform as expected in a given situation in which negative consequences are possible.” We embrace this perspective on trust to encompass the sociotechnical nature of digital platforms, recognizing the significance of both human-centric and technology-centric factors in cultivating trust. The synthesis of empirical findings from trust-related primary studies in the e-commerce context has been the focus of only a limited number of prior metaanalyses from the past. These meta-analyses have predominantly encompassed studies within broader domains such as e-commerce in general (He 2011; Kim and Peterson 2017), or related domains such as mobile commerce (Sarkar etal. 2020; L. Zhang etal. 2012), social commerce (Wang etal. 2016), mobile payment (Liu etal. 2019), and electronic services (Mou etal. 2017). Kim and Peterson’s (2017) meta-analysis of antecedents and consequences of trust in e-commerce context reveals a wide array of factors that play a major role in the trust-building process, including technology-related characteristics such as perceived security, privacy, information and design quality, with mean effect sizes ranging from moderate to very strong. Similarly, He (2011) found that deterrence-based, social and institution-based, and technological attributes-based factors emerge as the most influential contributors to trust development in the e-commerce context, whereas the impact of personal characteristics plays a minor role. In the mobile commerce context, prior studies (Sarkar etal. 2020; Zhang etal. 2012) consistently found that several constructs that are crucial in traditional technology acceptance like perceived usefulness, ease of use, enjoyment, and success factors such as system quality and satisfaction, and various other user-related factors such as attitude significantly influence trust in mobile commerce. In their examination of the influence of trust and risk on individual adoption behavior in social commerce, Wang etal. (2016) concluded that both trust and risk significantly affected individual behavior towards social commerce, with trust exhibiting a stronger impact. Similar findings were made by other studies for the mobile payment (Liu etal. 2019) and also for the electronic services context (Mou etal. 2017). While these previous meta-analyses have yielded valuable insights into trustrelated topics within their distinct technological context, none of them has provided a comprehensive understanding of the trust-building determinants in the specific context of digital platforms. By tailoring our focus on trust in the specific digital platform context, our study accounts for the specific multi-dimensional nature of digital platforms, while addressing a research gap that is relevant for both research and practice. For platform developers or operators, understanding the pivotal role of trust-building factors is essential to proactively shape the acceptance and use of digital platforms. Likewise, for scholars spanning various disciplines, an advanced understanding of the factors that shape individuals’ trust in digital platforms could refine their conceptual frameworks and guide their focus toward areas requiring deeper exploration. Overall, our research contributes to leveraging the cumulative knowledge base in the realm of digital platforms, since we additionally address the variability across individual studies by thoroughly conducting multiple subgroup analyses to further explore the conditions that may cause the trust antecedents to have varying impacts on the trusting intention. 3620 T.D.Oesterreich et al. 3 Research framework Due to the sociotechnical and multi-dimensional nature of digital platforms, trust unfolds across users’ trust in the platform operator, trust in the underlying technology, and trust in user interactions (Räisänen etal. 2021; Sutherland and Jarrahi 2018). Building upon this multi-dimensional view of digital platforms, we develop our research framework that encompasses trust-building factors associated with platform, provider, and user characteristics (cf. Fig.1). We provide a comprehensive elucidation of these structural and moderating variables in the subsequent sections. 3.1 Trust antecedents As explained in Sect.2, the dependent variable in our research framework is trusting intention in digital platforms, while a comprehensive set of trusting beliefs, technology acceptance factors, as well as factors pertaining to system, users, and platform provider are conceptualized as independent variables. Our conceptualization of trust draws from the cross-disciplinary framework of McKnight and Chervany (1996) which describes a trust-building process where trusting beliefs engender trusting intentions and subsequent trusting behavior. In this study, our emphasis is solely on Fig. 1 Research framework 3621 What drives individuals’ trusting intention indigital… trusting intention as a dependent variable, aligning with our main aim of examining trust-building factors within the context of digital platforms. Guided by trust theory (Lankton et al. 2015; Mcknight et al. 2011; McKnight and Chervany 2001), trusting beliefs constitute the first set of independent variables in our research framework. As outlined in Table1, human-like trusting beliefs encompass a range of trust antecedents, including integrity, ability, competence, and benevolence, all of which are used to characterize the individuals’ perceptions of the trustee’s principles, intentions, abilities, and behavior (Lankton etal. 2015; Mcknight etal. 2011). System-like trusting beliefs, including attributes like reliability, functionality, and helpfulness, share conceptual alignment with human-like trusting beliefs owing to their analogous meanings. However, they are utilized to capture the trustor’s perceptions regarding the system’s attributes, features, and performance, all of which are seen as beneficial for task fulfillment (Lankton etal. 2015). Technology acceptance factors, forming the second group of trust antecedents, represent variables commonly employed in primary studies to capture individuals’ acceptance of digital platforms (Benamati etal. 2010; Lin etal. 2011; Pavlou 2003). Grounded in the technology acceptance model (TAM) and the extended unified theory of acceptance and use of technology (UTAUT2), these factors have proven effective in predicting user behavior within the context of information systems. In particular, previous research highlights that perceived usefulness and perceived ease of use, two core variables in the technology acceptance model (TAM), are effective predictors of technology adoption behavior (Davis 1989; Wu etal. 2011). When exploring trust in digital platforms, scholars have also frequently incorporated perceived enjoyment (Sun and Zhang 2008; Tu etal. 2012) and perceived value (Kim 2012; Tam etal. 2020) into their research. While perceived usefulness and perceived ease of use are suitable for conceptualizing the usability aspects of a technology, perceived enjoyment and perceived value contribute to explaining hedonic and motivational factors (cf. Table1). Consequently, we regard these technology acceptance factors as valuable predictors in our meta-analysis study. According to trust theory (Lankton etal. 2015; Mcknight etal. 2011; McKnight and Chervany 2001), security and privacy are critical trust-building elements, while system quality, a key determinant from the IS success model (DeLone and McLean 2003), reflects users’ perceptions of a system’s overall quality. As a result, these factors are frequently employed to conceptualize the system characteristics of digital platforms in primary studies. Security pertains to individuals’ perception that transactions are secured when using the system (Kim and Ahn 2007). Therefore, when disclosing personal information, financial data, or other confidential details on a platform, security plays a significant role in building trust in digital platforms (Patro 2023). In conjunction with security, privacy is also recognized as a significant determinant of trust in the technology-trust literature. In our interconnected world, a substantial volume of data is generated, collected, and stored by an expanding array of individuals, devices, and sensors, capable of being accessed and shared to generate value (Tene and Polonetsky 2011). The existing literature underscores the importance of privacy significance in these data usage contexts and the growing necessity to balance the advantageous utilization of data with privacy safeguarding. Trust formation of individuals is rooted in their belief that authorized access and 3622 T.D.Oesterreich et al. Table 1 Overview of trust antecedents employed in the research framework Theory Variables Definition Examples Trusting beliefs Trust theory (Lankton etal. 2015; Mcknight etal. 2011; McKnight & Chervany 2001) Human-like trusting beliefs Individuals’ belief that the trustee acts in a predictable manner (integrity), shows the necessary abilities, skills, and competencies (ability/competence), and has good intentions and provides strong advice (benevolence) (Lankton etal. 2015; Mayer etal. 1995; Mcknight etal. 2011) (Cao etal. 2020; C. Chiu etal. 2009a, b; Gefen & Pavlou 2011) System-like trusting beliefs Individuals’ belief that the system shows a consistent and predictable behavior (reliability), provides the features necessary to complete a task (functionality), and provides users with useful help (helpfulness) (Lankton etal. 2015; Mcknight etal. 2011) (Cao etal. 2020; Kim etal. 2011; Mcknight etal. 2011) Technology acceptance factors TAM (Davis 1989; K. Wu etal. 2011), UTAUT2 (Venkatesh etal. 2012) Perceived usefulness Individuals’ perception that a system is helpful for completing their task (Davis 1989) (Benamati etal. 2010; Lin etal. 2011; Pavlou 2003) Perceived ease of use Individuals’ perception that a system is easy to use (Davis 1989) Perceived enjoyment Individuals’ perception that using an IT artifact is fun, enjoyable, or entertaining (Davis etal. 1992; Venkatesh etal. 2012) (Sun & Zhang 2008; Tu etal. 2012) Perceived value Individuals’ perception that using an IT artifact is beneficial (Chang etal. 2016) (Kim 2012; Tam etal. 2020) System characteristics 3629 What drives individuals’ trusting intention indigital… (Webster and Watson 2002). Thirdly, our literature search was not restricted solely to journal papers or conference proceedings; we also included book chapters, dissertations, working papers, and the so-called “grey literature” in our search process. Including grey literature in the meta-analysis allows us to comprehensively capture the breadth and depth of available data on the research topic, enhancing the overall research outcomes’ comprehensiveness while mitigating the potential impact of publication bias (Hansen etal. 2022; Kepes and Thomas 2018; Rothstein etal. 2005). Publication bias can arise when studies with non-significant results are less likely to be published, leading to an overrepresentation of significant results due to their greater likelihood of being published (Kepes and Thomas 2018; Rothstein etal. 2005). Therefore, in order to comprehensively capture a wide spectrum of relevant studies investigating the impact of specific factors on trust in digital platforms, we employed the Scopus and EBSCOhost databases in the first step. In both databases, we utilized the keyword combination “trust” AND (“digital platform” OR “online platform”) and restricted the search to article titles, abstracts, and keywords in order to refine the focus and relevance of the search results. Overall, this search strategy yielded a total number of 1,134 hits. In the next step, a screening process was conducted to identify relevant studies. Initially, duplicates were removed, leading to a reduction of 174 articles. Subsequently, the remaining 960 articles were assessed for relevance based on their titles and abstracts. In this screening step, we only considered articles for the eligibility assessment step that focus on studying trust in digital platforms. Therefore, the articles must report qualitative or quantitative findings on the relationship between trust antecedents and trusting intention. Hence, a set of inclusion and exclusion criteria, as presented in Table3, was established and consistently employed by two researchers throughout the screening and eligibility process. The aim of implementing these criteria was to eliminate evidently irrelevant articles and to identify those that held potential relevance to the research questions. A total of 642 studies were excluded based on their titles, as they were evidently not closely aligned with the topic. Following this, the abstract of each remaining article was reviewed, resulting in the elimination of an additional 121 articles due to the exclusion criteria, resulting in 197 articles remaining for full-text examination. Out of these 197 articles, 65 were excluded due to their lack of direct relevance to the investigated topic, including cases where other IT artifacts were considered or factors unrelated to trust were explored. Additionally, 55 studies were qualitative in nature, not meeting the requirements for meta-analysis. Efforts were made to retrieve missing data by contacting study authors, as suggested by Liberati etal. (2009), but 21 studies were eventually excluded due to data unavailability. Consequently, the comprehensive literature search and selection process resulted in the inclusion of 56 quantitative articles for the meta-analysis. Through subsequent forward and backward searches, an additional 16 articles were identified and included in the study sample. As a result, the total number of articles for meta-analysis reached 72, encompassing 74 primary studies. The complete literature search and selection procedure was conducted independently by two researchers, aligning with the interrater reliability principle to ensure consistency in the rating process (Landis and Koch 1977; LeBreton and Senter 2008). As per Landis and Koch (1977), the 3630 T.D.Oesterreich et al. Table 3 Inclusion and exclusion criteria Criteria Inclusion Exclusion Study focus Studies with a focus on trust in digital platforms Studies with a focus on trust in other IT artifacts Dependent variables Studies using dependent variables describing the trusting intention of users Studies using other dependent variables Independent variables Studies using independent variables describing trust-building factors in their research framework Studies using other independent variables in their research framework Publication type Published studies (journal articles, conference proceedings, book chapters) Unpublished studies (working papers and dissertations) Duplicate studies Bachelor’s and master’s theses or presentation slides Available data Studies reporting relevant data, including: Effect size measures Sample sizes Studies without relevant data, including: Studies that lack relevant data 3631 What drives individuals’ trusting intention indigital… Cohen’s Kappa value of 0.82 indicates substantial agreement, confirming the objectivity and reliability of the search outcomes. 4.2 Documentation andcoding ofstudies Following the completion of the literature search and selection process, our attention turned to the systematic documentation and coding of pertinent characteristics and quantitative data from the selected studies (Hunter and Schmidt 2004, p. 470). This task entails the choice oftheeffect size measure [Step 3 according to Hansen etal.’s (2022) guideline] and the conduct of the coding process itself [Step 6 of Hansen etal.’s (2022) approach]. Therefore, we developed a coding form that enabled us to systematically collect the key information of each study, including general study characteristics, moderators of interest, constructs and measures (cf. Table 4). To ensure accurate identification and categorization of the studies, we initially recorded general study characteristics, including study ID, publication date, publication status, and type. Despite conducting an extensive search as described in Sect.4.1, no unpublished articles were identified for inclusion and coding in the study sample. We also coded sample type and economic area as moderators, as defined in Sect.3.2, along with the relevant constructs and measures outlined in Table4. To code the data, we utilized the overview of the main independent, dependent, and moderating variables outlined in Sect.3. We established a coherent set of coding rules that were uniformly applied to the pertinent variables. Throughout the coding process, these rules were consistently based on the information provided in the studies. Concerning the independent variables as the constructs of interest, we relied on the items presented in each study before coding the variables, ensuring precision and consistency during the coding process. When selecting and coding theeffect size measure, indicating the strength of the observed effect concerning correlations between variables or groups (Field 2001), we adhered to the guidance of Peterson and Brown (2005) and Hunter and Schmidt (2004) by using the zero-order correlation matrices reported in the articles. In cases where neither correlation coefficients nor a zero-order correlation matrix were provided in the text, we solely coded β coefficients that denoted a straightforward bivariate (zero-order) association between two variables, aligning with Peterson and Brown’s (2005) recommendation. If such data were unavailable, the respective primary study was excluded, as this rigorous approach was essential to enhance the validity and robustness of our meta-analysis. Similar to the literature search and selection process, the entire coding procedure was carried out independently by two researchers, achieving an interrater reliability of 0.79. In instances of disagreement, conflicts were resolved through thorough discussions to ensure a comprehensive consensus. 4.3 Data analysis andsynthesis In the data analysis and synthesis phase, selecting the appropriate analytical method and software are two key considerations, as emphasized in Step 4 and Step 5 of Hansen etal.’s (2022) guideline. We employed Python, leveraging its 3632 T.D.Oesterreich et al. Table 4 Coding form General study characteristics Study ID Unique identification number for each study Publication date The year in which the study was published or available Sample size Sample size of each study Publication status and type Published The study is published in journals, conference proceedings, or magazines Not published The study is not published (dissertations, reports, and other gray literature) Moderators Sample type Student sample Data were gathered from student participants Non-student sample Data were gathered from non-student participants Economic area Developed economy Data were collected from respondents located in a developed economy according to the classification of the United Nations (2019) Developing economy Data were collected from respondents located in a developing economy according to the classification of the United Nations (2019) Mixed Studies that report data collected from respondents located in several geographical areas Constructs Independent variable (original) Name of the original construct in primary studies (example: website quality) Independent variable (synthesized) Name of the synthesized construct in meta-analysis (example: system quality) Measures Effect size measure Correlations Effect size measures in the form of correlations (extracted from correlation matrices) Path coefficients Effect size measures in the form of path coefficients that reflect a simple bivariate (zero-order) relationship between two variables Effect size Magnitude of the effect size measure 3633 What drives individuals’ trusting intention indigital… Table 4 (continued) General study characteristics Comments Issues to be discussed and conflicts to be resolved 3634 T.D.Oesterreich et al. vast array of packages, for tasks ranging from data preparation to visualization in our analysis of the 74 quantitative studies included. We opted for univariate metaanalysis and meta-regression analysis as our analytical methods, as they are best suited to address the research questions. Our dataset revealed 169 distinct relationships between independent and dependent variables. It should be noted that a single study might explore multiple such relationships. For studies that investigated several effects between similar variable categories, we derived a mean score. By doing so, we refrained from evaluating each relationship independently based on correlation and sample size, which facilitates a more consistent comparison of effects across outcomes. This also ensures that each individual observation is fairly represented within the larger sample (Cram etal. 2019; Mandrella et al. 2020). Subsequently, we employed a Fisher’s Z transformation to standardize the correlations from the coded or converted data (Field 2001). We then computed additional metrics for each study, which included confidence intervals (CIs), p-values, standardized residuals, and relative weights derived from the sample size. For ease of interpretation, these estimates were later converted back to correlations (Borenstein etal. 2011). The data analysis step is guided by Step 7 of Hansen etal.’s (2022) guideline, including model choice and robustness tests. Our analytical procedures were rooted in the methodologies described by Borenstein etal. (2011), specifically utilizing the random effects (RE) model as detailed in Table 5. Our decision to adopt the RE model for the meta-analysis arose from our assumption that different studies might be sampling from different populations, implying that observed variations in effect sizes might not solely result from sampling error but also from genuine between-study differences. The RE model, therefore, is suitable as it accommodates for both within-study and between-study variability. On the other hand, fixed effects models only account for within-study variability (Borenstein etal. 2010; Schmidt etal. 2009). Given the heterogeneity of our sample set and the variability in outcomes, the fixed effects model was deemed less fitting and thus excluded from our analysis. Table 5 Meta-analysis formulas, adapted from Borenstein etal. (2011) Where:r = sample correlation coefficient Vb = between-study variance zi = Fisher’s z score of the study i n = sample size of the study i Formula Description z i=0.5 ∗ln 1+r 1−r Transformation of sample correlation to Fisher’s z scale V z= 1 n−3 Variance of the Fisher’s z w i= 1 V z +V b Weight for each study, calculated as the inverse of the sum of the variance of the z-score and the between-study variance for that study z = ∑ (wi∗zi ) ∑ w i Random-effects weighted mean of the z-scores across all studies r z=e 2z − 1 e 2z +1 Conversion of the weighted mean z-score back to a correlation coefficient 3635 What drives individuals’ trusting intention indigital… To decipher the factors contributing to the observed variability, we utilized a partition test for categorical variables, specifically sample type and economic area, and executed a meta-regression for the continuous variable, publication year. This strategy was designed to evaluate effect sizes and variance across subgroups (King and He 2005). The partition test led to individual meta-analyses for each moderator, facilitating comparisons of summarized effects. This helped elucidate whether trust in digital platforms fluctuated based on publication years, sample types, or economic settings. We complement the data analysis step (Step 7) by conducting outlier analyses andtests forpublication bias, as recommended by Hansen etal. (2022). In particular, to ensure the credibility of our findings, we determined the Fail-safe N-value. This metric represents the count of absent or unpublished studies with non-significant outcomes that would be necessary to invalidate our meta-analysis results, thereby negating the observed relationship between the investigated variables (Borenstein etal. 2011). 5 Results In Sect.5, we present the meta-analysis outcomes from 14 bivariate relationships outlined in our research model [Step 8 of Hansen etal.’s (2022) guideline]. These findings illuminate the influences of various trust antecedents on shaping trusting intentions towards digital platforms. Committed to the integrity of our research, we thoroughly examine the potential ramifications of outliers and publication bias. Section5.2 delves deeper, offering an exploration of the heterogeneity in observed effects. Through a suite of statistical tools and assessments, we clarify the nuances in effect size disparities among studies. Finally, in Sect.5.3, we shift our focus to moderator effects that are potentially the origin of the observed variations in true effects. 5.1 Meta‑analysis results We carried out 14 distinct meta-analyses corresponding to each bivariate relationship in our research model. The outcomes of these analyses are consolidated in Table6. These outcomes indicate that the weighted summary effect sizes, with trusting intention as the dependent variable, differ based on the trust antecedents and other independent variables. Notably, the summary effect sizes oscillate between 0.31 for FAM and 0.63 for HTB. All these effect sizes differ significantly from zero, evidenced by a 95% CI in the positive spectrum and statistically significant Z-val- ues. These Z-values surpass the critical Z thresholds of 1.96 (for p < 0.05) and 3.29 (for p < 0.001). Following the categorization proposed by Lipsey and Wilson (2001) — where effect sizes are considered small (≤ 0.30), medium (0.30–0.50), large (0.50–0.67), or very large (≥ 0.67) — our data reveals large effects for the relationships between HTB (0.63), PU (0.52), PENJ (0.53), ATT (0.56), IMG (0.54), and SASS (0.53) and 3636 T.D.Oesterreich et al. trusting intentions. The remaining relationships are situated in the medium bracket. This suggests that both trusting beliefs and factors related to technology acceptance, system and user characteristics, as well as platform provider characteristics are influential drivers in reinforcing trusting intentions. To underscore the precision and credibility of our results, we examined the potential impact of outliers and publication bias (Kepes and Thomas 2018). We visually assessed the forest plots and executed meta-analyses for each relationship while systematically omitting one study during each iteration. Although the visual assessment disclosed variances in effect sizes across studies, it did not pinpoint a definitive outlier. The n-1 iterative meta-analyses revealed only marginal variations across each relationship, indicating that outliers exert minimal influence. To enhance the credibility of our summary effect sizes, we probed for potential publication bias. Utilizing Rosenthal’s (1979) Fail-safe N methodology, we sought to determine if the observed influence of the antecedents on trusting intentions might be primarily attributed to publication bias. Rosenthal’s Fail-safe N quantifies the number of studies necessary Table 6 Meta-analysis results IV, independent variable; HTB, Human-like trusting beliefs; STB, System-like trusting beliefs; PU, Perceived usefulness; PEOU, Perceived ease of use; PENJ, Perceived enjoyment; PVAL, Perceived value; SECU, Security; PRIV, Privacy; SYSQ, System quality; ATT, Attitude; FAM, Familiarity; IMG, Image/ reputation; SERQ, Service quality; SASS, Structural assurance. n, number of studies; N, sample size; ES, estimated summary effect size; 95% CI, 95% confidence interval [lower bound, upper bound]; Z, Z value; Q, Q value; Df, degree of freedom; τ2, between-study variance; 80%-PI, 80% prediction interval [lower bound, upper bound]; FS-N, Failsafe-N; significance: ** p < .01, * p < .05, not significant (n.s.) for p > .05 IV n N ES 95% CI Z Q Df τ280%-PI FS-N Trusting beliefs HTB 10 4,380 0.63 [0.54, 0.71] 49.41** 224.94** 9 0.05 [0.42, 0.78] 29 STB 4 1,360 0.42 [0.25, 0.57] 16.67** 53.87** 3 0.04 [0.17, 0.62] 5 Technology acceptance factors PU 25 6,856 0.52 [0.47, 0.57] 47.56** 202.11** 24 0.03 [0.35, 0.66] 290 PEOU 30 8,827 0.47 [0.41, 0.53] 48.05** 407.13** 29 0.04 [0.24, 0.65] 463 PENJ 5 1,501 0.53 [0.36, 0.67] 22.91** 89.49** 4 0.06 [0.25, 0.73] 8 PVAL 10 3,401 0.44 [0.29, 0.57] 27.35** 259.27** 9 0.07 [0.10, 0.68] 48 System characteristics SECU 13 4,642 0.48 [0.40, 0.55] 35.45** 137.77** 12 0.03 [0.29, 0.63] 78 PRIV 7 2,310 0.45 [0.26, 0.61] 23.29** 197.18** 6 0.08 [0.09, 0.71] 21 SYSQ 17 6,513 0.45 [0.36, 0.53] 38.69** 324.35** 16 0.05 [0.19, 0.65] 148 User characteristics ATT 8 3,453 0.56 [0.44, 0.66] 37.26** 182.26** 7 0.05 [0.32, 0.74] 22 FAM 8 2,557 0.31 [0.20, 0.40] 15.93** 56.44** 7 0.02 [0.12, 0.47] 38 Platform provider characteristics IMG 21 7,422 0.54 [0.48, 0.60] 51.99** 295.17** 20 0.04 [0.34, 0.69] 192 SERQ 14 5,468 0.46 [0.36, 0.54] 36.32** 242.16** 13 0.04 [0.21, 0.64] 96 SASS 11 3,701 0.53 [0.44, 0.61] 36.09** 152.45** 10 0.04 [0.32, 0.70] 48 3637 What drives individuals’ trusting intention indigital… to achieve a non-significant point estimate in the meta-analysis (p > 0.05). The Failsafe N values extracted from our meta-analyses imply that neutralizing the observed effect would necessitate anywhere from a few to several hundred studies. Nonetheless, some Fail-safe N values seemed modest, reflecting the constrained number of studies analyzed. 5.2 Heterogeneity ofobserved effects We investigated whether moderating factors led to differences in effect sizes across studies by applying various heterogeneity statistics. We started with Cochran’s Q-test of homogeneity (1954), setting a null hypothesis that each study has an identical true effect and any observed variations in effect sizes arise solely from sampling error. The Q-value for each relationship between an independent variable and trusting intentions was computed to measure the dispersion of observed effects around the mean (Borenstein etal. 2017). Should Q surpass the critical Q on a χ2 distribution with n − 1 degrees of freedom, where n is the number of studies, we would reject the null hypothesis (Borenstein etal. 2017; Cram etal. 2019; Huedo- Medina etal. 2006). Given that all Q-values were significant (p < 0.01), it was clear that sampling error alone could not account for the heterogeneity. We recognized a notable variance between studies, affirming the foundation of the RE model (Huedo- Medina etal. 2006). This variance is articulated by τ2, with estimates from 0.02 to 0.08. The proportion of observed variance accounted for by τ2 is indicated by I2, which varied from 87 to 97% (Borenstein etal. 2017). Another measure is the prediction interval which provides insights into variations in the true effect size (Borenstein etal. 2017; Whitener 1990). This range represents potential true effects in subsequent studies exploring trusting intentions in digital platforms in comparable contexts to our sample (IntHout etal. 2016). Conforming to prior meta-analyses on IT-related topics, we derived 80% prediction intervals (Cram etal. 2019; Gerow etal. 2014; Mandrella etal. 2020). The resulting wide-ranging prediction intervals, coupled with our earlier tests, underscore the role of moderating influences (Kepes etal. 2013; Whitener 1990). 5.3 Moderator analyses Our preliminary tests indicated potential moderator effects that might clarify the observed variability in true effects. Consistent with our research model, we classified most moderators as discrete variables, with the exception of the publication year, which was treated as a continuous variable. To dissect these effects further, we grouped studies according to the economic area and sample type. This process, however, left us with a diminished subset of the original studies for further examination. Although there is no universally agreed-upon minimum sample size to guarantee sufficient power for meta-analyses, standards set by meta-analysts examining IT-related topics typically range from a modest three studies (Tao etal. 2020) up to fifteen (Yun etal. 2014). A challenge we faced was that several subgroups had minimal representation, sometimes as few as just 1–3 studies. Depending on such limited samples can, in 3638 T.D.Oesterreich et al. certain situations, offer little to no insight, and it becomes arguable if a single study can represent a comprehensive meta-analysis. Despite this, we have chosen to include all subgroup findings in our report for the sake of transparency and thoroughness, while acknowledging their potentially limited interpretive power. Our analyses in Tables7 and 8 indicate that, even when segmented into subgroups, all effects are notably non-zero for both the economic area and sample type categories. This conclusion is supported by the positive spectrum of the 95% CIs and significant Z-values. Notably, for variables with a relatively larger pool of studies even post-segmentation (e.g., PU, PEOU, IMG), we observed overlapping 95% CIs within moderator categories. This overlap suggests that the summary effect sizes are not significantly varied (Mandrella etal. 2020). When we zeroed in on the variance, our findings still pointed towards broad 80% prediction intervals and, in some cases, intervals bordering zero, coupled with statistically significant between-study variances. This indicates that the discrete moderators we examined might not be the ideal candidates to elucidate the differences observed across studies. We evaluated the influence of the continuous moderator, publication year, using meta-regressions. The outcomes of these analyses can be found in Table9. Among the relationships analyzed, only the one between SERQ and trusting intentions yielded a significant regression coefficient of − 0.054, Z = − 5.09, p < 0.01). This suggests that over time, the impact of service quality on trusting intentions diminishes (cf. Fig.3). 6 Discussion Recognizing the need for a more comprehensive accumulation of knowledge concerning digital platforms (Sutherland and Jarrahi 2018), especially within the realm of trust-building mechanisms (Räisänen etal. 2021), our study aimed to fulfil the primary research objective of integrating empirical findings from prior studies regarding the drivers of trust in digital platforms (RQ1). Furthermore, we sought to address the question of how the influence of these factors on individuals’ trusting intentions might vary across primary studies under specific conditions (RQ2). The findings unveil a diverse array of trust antecedents that wield a significant impact on the trusting intention in digital platforms, encompassing factors related to trusting beliefs, technology acceptance, and the characteristics of system, user, and platform providers. Additionally, we detected indications of a temporal effect within the relationship between service quality and trusting intention, underscoring the dynamic nature of these relationships over time. We engage in an in-depth discussion of these outcomes, delving into their implications for both research and practice in the next sections. These derived insights are conveniently encapsulated in Table10 for better reference. 6.1 Implications forresearch andpractice Conducting an extensive literature search across interdisciplinary databases, we identified 74 empirical studies from 72 academic journal articles and conference proceedings that quantitatively explore trust-related aspects in digital platforms, 3645 What drives individuals’ trusting intention indigital… Table 10 (continued) Main Findings Implications for research and practice MF4: The motivational factors perceived enjoyment (intrinsic motivator) and perceived value (extrinsic motivator) have received relatively less attention in primary studies • IP3: System designers should place greater emphasis on hedonic motivational factors by incorporating design elements that enhance users’ intrinsic motivation (such as fun or perceived enjoyment) during their interactions with the platform • IR3: Scholars should focus on addressing how different motivational factors (intrinsic versus extrinsic motivation) interact to drive individuals’ trusting intention in different platform contexts (hedonic versus utilitarian systems) • IR3.1: What design requirements and design principles are crucial for effectively enhance users’ intrinsic motivation (such as fun or perceived enjoyment) during their interactions with the platform in the context of digital platforms? • IR3.2: Do motivational factors (intrinsic versus extrinsic motivators) differ for varying platform contexts? • IR3.3: Is there a causal relationship between intrinsic and hedonic motivators in varying platform contexts? MF5: System characteristics related to security, privacy, and system quality have moderate impacts on trusting intention • IP4: System developers should maintain high standards of security, privacy, and system quality in digital platforms through the implementation of effective transaction management MF6: Users’ attitude toward the platform strongly influences trusting intention, whereas familiarity plays a moderate role • IR4: Scholars should investigate the interplay between attitude, trust antecedents, and trusting intention in digital platforms using a meta-analytic structural equation modeling approach • IR4.1: Do attitude and familiarity directly impact trusting intention, or do they serve as mediators between technology acceptance factors and trusting intention? • IR4.2: In what configurations do these factors play a crucial role in the context of digital platforms? • IR4.3: Does the importance of attitude and familiarity vary across different platform contexts? MF7: The image/reputation of a platform provider and the offered structural assurance are strong predictors of trusting intention, while service quality shows a medium effect • IP5: Platform providers are well advised to uphold their image and reputation by maintaining a positive track record, implementing effective feedback mechanisms, and employing strategic advertising and publicity efforts • IP6: Managers and system designers should diligently ensure and visually demonstrate the presence of securing contextual and environmental conditions that offer users the essential infrastructure supporting the utilization of the digital platform, encompassing the legal and contractual framework MF8: The image/reputation of the platform provider is among the most frequently studied trust antecedents in our sample 3646 T.D.Oesterreich et al. trust in digital platforms could offer valuable insights for scholars and practitioners across various disciplines, aiding them in effectively cultivating individuals’ trust and shaping usage behaviour. Consequently, we suggest: Implication for research (IR2): Scholars should redirect their focus from wellestablished technology acceptance factors, such as perceived usefulness and perceived ease of use, to less explored factors that can deepen our understanding of how digital platforms can effectively shape individuals’ behaviour (e.g., toward more sustainability or more desired behaviour). The following research questions remain for future research endeavors. • IR2.1: What factors, beyond the well-established technology acceptance factors “perceived usefulness” and “perceived ease of use”, are essential for effectively fostering individuals’ acceptance of digital platforms (such as perceived sustainability or others)? (Exploratory research on these factors needed) • IR2.2: Do these alternative technology acceptance factors (such as perceived sustainability) differ for varying platform contexts (e.g. digital platforms for sustainable products and services versus digital platforms for conventional products and services)? For practice, the following implications could be derived from the main findings: Implication for practice (IP3): System designers should place greater emphasis on hedonic motivational factors by incorporating design elements that Table 10 (continued) Main Findings Implications for research and practice RQ2: To what extent do moderating factors cause these trust-building factors to have varying impacts on individuals’ trusting intention in digital platforms? MF9: Sample type (student/non-student sample) and economic area (developed/developing area) do not exhibit moderating effects on trusting intention • IR5: Scholars should delve into investigating the interplay between service quality, image/reputation, and individuals’ trusting intention in digital platforms, employing a meta-analytic structural equation modeling approach. This method would provide a more comprehensive understanding of both the direct and indirect relationships between service quality and trusting intention • IR5.1: Does service quality have an indirect influence on trusting intention through other trustbuilding factors (such as image/reputation), rather than having a direct impact on trusting intention? • IR5.2: In which platform contexts does service quality hold greater importance? • IP7: Managers and system designers should avoid exclusively focusing on service quality as a means to foster user trust, as delivering good service alone may not be enough. Instead, they should take into account the entire spectrum of trustbuilding factors as highlighted in our study MF10: Publication year moderates the relationship between service quality and trusting intention, with service quality showing a declining impact on trusting intention over time 3647 What drives individuals’ trusting intention indigital… enhance users’ intrinsic motivation (such as fun or perceived enjoyment) during their interactions with the platform. Implication for research (IR3): Scholars should focus on addressing how different motivational factors (intrinsic versus extrinsic motivation) interact to drive individuals’ trusting intention in different platform contexts (hedonic versus utilitarian systems), including the following questions: • IR3.1: What design requirements and design principles are crucial for effectively enhance users’ intrinsic motivation (such as fun or perceived enjoyment) during their interactions with the platform in the context of digital platforms? • IR3.2: Do motivational factors (intrinsic versus extrinsic motivators) differ for varying platform contexts? • IR3.3: Is there a causal relationship between intrinsic and hedonic motivators in varying platform contexts? Regarding system characteristics, the outcomes of our meta-analysis are surprising in demonstrating that factors linked to security, privacy, and system quality exhibit only moderate effects on trusting intention (MF5). Given the potential risks tied to financial transactions within digital platforms (Gefen, 2000), we would have anticipated these associations to fall within the range of strong to very strong impacts. Nevertheless, even though the influence on trusting intention is of medium strength, security, privacy, and system quality remain significant factors in shaping individuals’ trust in digital platforms. This concept is often referred to as “the logistics of transaction” in the literature, encompassing effective transaction management (Sutherland and Jarrahi 2018, p. 10). Consequently, we propose the following recommendations: Implication for practice (IP4): System developers should maintain high standards of security, privacy, and system quality in digital platforms through the implementation of effective transaction management. When exploring the role of user characteristics in driving trust, we further found that individuals’ attitude toward the platform strongly influences trusting intention, whereas familiarity plays a moderate role (MF6). Hence, the findings suggest that individuals’ attitude, representing their predisposition to respond favorably or unfavorably toward the system (Benamati etal. 2010), is more likely to foster trust in digital platforms compared to familiarity, which pertains to individuals’ past experiences with the system (Kim etal. 2008). However, a pertinent question in this context is whether attitude might function as a direct precursor to trust or as an intermediary factor that influences relationships related to trust in the specific context of digital platforms. For instance, familiarity with a digital platform could exert effects on attitude that subsequently influence trust, or vice versa. In this context, scholars have shown that technology attitude serves as a mediator between technology acceptance factors and intention to use, indicating that the impact of perceived usefulness and trusting beliefs on intentions is dependent on the formation of a technology attitude or trusting attitude (Benamati etal. 2010). Therefore, we propose this question as a promising avenue for future research: 3648 T.D.Oesterreich et al. Implication for research (IR4): Scholars should investigate the interplay between attitude, trust antecedents, and trusting intention in digital platforms using a meta-analytic structural equation modeling approach. Particularly, the following research questions could be added to the research agenda: • IR4.1: Do attitude and familiarity directly impact trusting intention, or do they serve as mediators between technology acceptance factors and trusting intention? • IR4.2: In what configurations do these factors play a crucial role in the context of digital platforms? • IR4.3: Does the importance of attitude and familiarity vary across different platform contexts? Closely intertwined with individuals’ familiarity with a digital platform are attributes like the image/reputation of a platform provider and the provided structural assurance, both of which have demonstrated strong predictive power for trusting intention, whereas service quality exhibits a moderate effect (MF7). The image or reputation of a digital platform emerges from users’ past experiences with the digital platform, representing the level of esteem that individuals hold for a platform provider based on its track record of fulfilling or meeting its obligations towards other individuals in the past (Kim etal. 2008). Therefore, users’ positive experiences with a digital platform would help building a positive image and reputation among customers, e.g. through effective feedback mechanism (Pavlou and Gefen 2004), as well as advertising and publicity strategies (Kim and Ahn 2007). The significant impact of image and reputation on fostering trust in digital platforms becomes evident when considering that this factor ranks among the most extensively examined trust antecedents in our sample (n = 20) (MF8). As a result, the following implication arises for practitioners: Implication for practice (IP5): Platform providers are well advised to uphold their image and reputation by maintaining a positive track record, implementing effective feedback mechanisms, and employing strategic advertising and publicity efforts. In relation to structural assurance, the strong impact on trust suggests that individuals are inclined to develop trust in digital platforms when there are contextual conditions (such as promises, contracts, regulations, and guarantees) in place that facilitate the use of the system (McKnight et al. 1998). This may be particularly important in specific application contexts such as for m-payment systems (Chandra etal. 2010) in which financial losses may be high without such securing structural assurances. In this context, scholars from various disciplines have advocated for an optimal balance between regulations and security concerns (Räisänen etal. 2021; Sidorenko 2022), underscoring the need to explore how much structural assurance is truly conducive to fostering user trust. Therefore, we suggest: Implication for practice (IP6): Managers and system designers should diligently ensure and visually demonstrate the presence of securing contextual and environmental conditions that offer users the essential infrastructure supporting the utilization of the digital platform, encompassing the legal and contractual framework. 3649 What drives individuals’ trusting intention indigital… Regarding RQ2, we discovered that there were no moderating effects of sample type (student/non-student sample) and economic area (developed/developing area) on trusting intention across the bivariate relationships (MF9), with the exception of one instance. In particular, we observed that publication year moderates the relationship between service quality and trusting intention, with service quality showing a declining impact on trusting intention over time (MF10). This finding suggests that individuals assign decreasing importance to service quality as time progresses, while other factors (such as image/reputation, privacy and security) maintain consistent importance over the years. There may be several plausible explanations for this finding. First, the intensifying competition within the digital platform landscape may have compelled platforms to consistently enhance their features and services to both attract and retain users. In such a competitive environment, users might become less willing to rely solely on service quality as a basis for trusting a platform, given the multitude of other influential factors shaping their trusting intention. Consistently, previous research has demonstrated that perceptions of quality alone are not sufficient to enhance customer satisfaction in online shopping contexts. This is attributed to the fact that service quality is a necessary but not sufficient condition for driving customer satisfaction. Rather, service quality is expected to serve as a hygiene factor, being responsible for maintaining satisfaction levels (Fang etal. 2011). Within the context of design studies, scholars have even highlighted that conventional evaluation criteria for IS design, such as user acceptance or system quality, might not adequately address the complexities inherent to platforms (de Reuver etal. 2018). Second, as a larger number of users engage with digital platforms over time, the influence of image and reputation on shaping users’ trusting intentions may have become more pronounced. Shared positive or negative experiences from others could potentially outweigh the direct impact of service quality on trusting intentions, potentially resulting in a weakening of the observed relationship. This perspective aligns with a recent study, which underscores the considerable contribution of service quality and customer experience to formulating the corporate image of service providers. Moreover, this study underscores that corporate image serves as a substantial mediator within the relationship between service quality and customer commitment (Yingfei etal. 2022). Therefore, it is possible that service quality now exerts an indirect influence on trusting intention through image/reputation, rather than having a direct impact on trusting intention. Accordingly, we suggest: Implication for research (IR5): Scholars should delve into investigating the interplay between service quality, image/reputation, and individuals’ trusting intention in digital platforms, employing a meta-analytic structural equation modeling approach. This method would provide a more comprehensive understanding of both the direct and indirect relationships between service quality and trusting intention. The following research questions are worthwhile for future research avenues: • IR5.1: Does service quality have an indirect influence on trusting intention through other trust-building factors (such as image/reputation), rather than having a direct impact on trusting intention? • IR5.2: In which platform contexts does service quality hold greater importance? 3650 T.D.Oesterreich et al. Implication for practice (IP7): Managers and system designers should avoid exclusively focussing on service quality as a means to foster user trust, as delivering good service alone may not be enough. Instead, they should take into account the entire spectrum of trust-building factors as highlighted in our study. In summary, the results of our meta-analysis indicate that individuals’ trust in digital platforms is influenced by a diverse array of trust-building factors originating from various spheres of digital platforms such as system, user, and platform provider characteristics. These findings contribute significantly to both research and practice by synthesizing evidence from 74 primary studies across 72 articles, thereby substantiating the impact of a wide range of trust-building factors on individuals’ trust in digital platforms. In response to the call for cumulative knowledge in digital platform research (Sutherland and Jarrahi 2018), our study provides a more profound understanding of the trustbuilding mechanism within the digital platform context, which holds paramount importance for the management and IS discipline in the digital era. However, it’s worth noting that, as highlighted by scholars, the research field centered around digital platforms is presently situated in the intermediate phase of the maturity curve (de Reuver etal. 2018). Consequently, we encourage both management and IS scholars to persist in their research efforts concerning trust-related aspects within the realm of digital platforms. 6.2 Limitations The current meta-analysis presents some inherent limitations that warrant consideration while interpreting its findings. Foremost among these is its exclusive reliance on published studies. Despite rigorous efforts to comb the literature, we could not identify any unpublished articles. This potential publication bias might inflate the observed effect sizes or could distort the genuine relationship between the examined variables. To mitigate this concern, we employed the Fail-Safe N method. However, it is crucial to recognize that the Fail-Safe N doesn’t entirely account for the potential existence of unpublished studies that might have yielded less significant outcomes. Such studies could further shape our understanding of the influence of independent factors on trust (Lipsey and Wilson 2001). Another limitation of the meta-analysis method lies in the broad consideration of various trust definitions of the dependent variable, resulting in high heterogeneity of results and impairing the comparability of studies. This limitation highlights the challenge known as the “apples and oranges” problem, which questions the validity of findings resulting from the integration and analysis of different variables, measures, and study designs with varying objectives (Hwang 1996). Furthermore, it’s crucial to acknowledge that the findings of our meta-analyses might be influenced by a certain degree of “personal bias” stemming from individual assumptions and interests (Wanous etal. 1989). However, several methods were implemented to address this bias, encompassing a thorough documentation of the literature search and selection process using a PRISMA flowchart. Additionally, coding rules and forms were established to guide the entire coding procedure, accompanied by the calculation of interrater reliability (LeBreton and Senter 2008). Furthermore, the interpretation of our results is subject to several limitations. One key concern is the outcome of our partition tests, which did not reveal 3651 What drives individuals’ trusting intention indigital… statistically significant differences in effect sizes across the moderator subgroups of sample type and economic area. This suggests that our moderator analyses were not able to explain the observed heterogeneity in the effect sizes, pointing to a dispersion among primary studies that warrants further exploration in subsequent research. Nevertheless, a notable finding was the discernible impact of the publication year on the results concerning the correlation between service quality and trusting intention. This emphasizes a temporal effect on the outcomes. Another limitation is the inherent tendency of meta-analyses to prioritize empirical research outcomes, often at the expense of qualitative insights. To offset this limitation, we incorporated qualitative findings into our interpretation and discussion, ensuring a more comprehensive understanding of our results ( King and He 2005). 7 Conclusion The aim of this study was to explore the factors influencing individuals’ trust in digital platforms (RQ1) and the impact of moderating effects on this relationship (RQ2). To address these questions, we performed a meta-analysis using data from 74 studies across 72 articles. The results indicate that all trust antecedents, encompassing trusting beliefs, technology acceptance factors, as well as system, user, and platform provider characteristics, have a significant and positive influence on trusting intention within the context of digital platforms. Analyzing the magnitude of summary effect sizes, we can conclude that human-like trusting beliefs, attitude, platform provider image/reputation, structural assurance, perceived enjoyment, and perceived usefulness as technology acceptance factors exhibit the strongest impact on trusting intention within digital platforms. On the other hand, familiarity, perceived value, and system-like trusting beliefs demonstrate a more moderate, yet still significant impact on trusting intention. Furthermore, our moderator analyses revealed a significant influence of publication year on the relationship between service quality and trusting intention, highlighting the presence of a temporal effect on the observed outcomes. The findings of our meta-analysis serve to consolidate knowledge about trustbuilding factors within the digital platform context, drawing on empirical evidence from prior research. Furthermore, these findings have significant implications for both research and practice, pinpointing potential avenues for future investigation and advancing our understanding of major trust-building elements in the realm of digital platforms. This comprehensive understanding, in turn, supports scholars and practitioners across research disciplines in delving into less explored factors and promoting in-depth exploration. Moreover, accumulating insights into critical trust-build- ing components in digital platforms can effectively steer platform development and design, ultimately enhancing user experiences through the delivery of more thoughtfully designed digital platforms. Appendix A: Articles included inthemeta‑analysis See Table11. 3652 T.D.Oesterreich et al. Table 11 Articles included in the meta-analysis (n = 72 articles / 74 studies) References Sample size Economic area Sample type IV category IV classification Effect size Agag and El-Masry (2016) 495 Developing economies Non-student Technology acceptance factors Perceived ease of use 0.583 Perceived usefulness 0.639 Perceived value 0.681 User characteristics Attitude 0.819 Provider characteristics Image/reputation 0.708 Akhmedova etal. (2021) 235 Developed economies Non-student Provider characteristics Service quality 0.440 Structural assurance 0.440 System characteristics System quality 0.300 Al-Debei etal. (2015) 273 Developing economies Non-student System characteristics System quality 0.110 Technology acceptance factors Perceived value 0.060 Provider characteristics Image/reputation 0.060 Al-kfairy etal. (2024) 267 Developing economies Student Vendor characteristics Structural assurance 0.640 Trusting beliefs Humanlike trusting beliefs 0.664 Alnaim and Abdelwahed (2023) 324 Developing economies Student Provider characteristics Service quality 0.328 Azhari etal. (2023) 203 Developing economies Mixed Provider characteristics Service quality 0.137 Image/reputation 0.193 Benamati etal. (2010) 383 Developed economies Student Technology acceptance factors Perceived ease of use 0.420 Perceived usefulness 0.610 User characteristics Attitude 0.640 Cao etal. (2020) 301 Developing economies Student System characteristics System quality 0.390 Provider characteristics Service quality 0.380 Image/reputation 0.430 Trusting beliefs Human-like trusting beliefs 0.465 System-like trusting beliefs 0.250 3653 What drives individuals’ trusting intention indigital… Table 11 (continued) References Sample size Economic area Sample type IV category IV classification Effect size Chameroy etal. (2024) 908 Developed economies Non-student Vendor characteristics Image/reputation 0.658 Trusting beliefs Humanlike trusting beliefs 0.801 Chellappa and Pavlou (2002) 179 Developed economies Student System characteristics Security 0.475 Provider characteristics Image/reputation 0.550 Chiu etal. (2009a, b) 360 Developing economies Non-student System characteristics Privacy 0.700 Technology acceptance factors Perceived ease of use 0.670 Perceived enjoyment 0.730 Perceived usefulness 0.660 Trusting beliefs Human-like trusting beliefs 0.760 System-like trusting beliefs 0.640 Chiu etal. (2009a, b) 311 Developing economies Non-student Technology acceptance factors Perceived ease of use 0.490 Perceived usefulness 0.600 Chiu etal. (2010) 412 Developing economies Non-student Provider characteristics Structural assurance 0.490 Diegman etal. (2018) 726 Developed economies Mixed Provider characteristics Image/reputation 0.473 System characteristics System quality 0.419 Elshaer etal. (2024) 570 Developing economies Non-student User characteristics Attitude 0.523 Fang etal. (2011) 219 Developing economies Non-student System characteristics System quality 0.635 Provider characteristics Structural assurance 0.750 Service quality 0.760 Faqih (2011) 281 Developing economies Mixed Technology acceptance factors Perceived ease of use 0.251 Perceived usefulness 0.383 Fernandes etal. (2024) 351 Developing economies Non-student Technology acceptance factors Perceived enjoyment 0.540 Technology acceptance factors Perceived value 0.370 User characteristics Familiarity 0.180 3654 T.D.Oesterreich et al. Table 11 (continued) References Sample size Economic area Sample type IV category IV classification Effect size Gefen and Pavlou (2011) 396 Developed economies Non-student Trusting beliefs Human-like trusting beliefs 0.420 Gefen etal. (2003) 213 Developed economies Student Technology acceptance factors Perceived ease of use 0.560 Ghofar etal. 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