Unfolding IoT Adoption: A Status Quo Bias Perspective

Rimbeck, Marlen; Stumpf-Wollersheim, Jutta; Richter, Alexander Article — Published Version Unfolding IoT Adoption: A Status Quo Bias Perspective Business & Information Systems Engineering Suggested Citation: Rimbeck, Marlen; Stumpf-Wollersheim, Jutta; Richter, Alexander (2024) : Unfolding IoT Adoption: A Status Quo Bias Perspective, Business & Information Systems Engineering, ISSN 1867-0202, Springer Fachmedien Wiesbaden, Wiesbaden, Vol. 67, Iss. 6, pp. 815-832, https://doi.org/10.1007/s12599-024-00891-6 This Version is available at: https://hdl.handle.net/10419/333364 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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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ RESEARCH PAPER Unfolding IoT Adoption: A Status Quo Bias Perspective Marlen Rimbeck •Jutta Stumpf-Wollersheim •Alexander Richter Received: 7 September 2022 / Accepted: 11 May 2024 / Published online: 31 August 2024 ÓThe Author(s) 2024 Abstract Internet of Things (IoT) solutions are still far from using their enormous potential, partly because misconceptions lead employees to avoid using IoT solutions and stick to established working routines. To shed light on the non-rational perspective of users, which allows for inference on the emergence of cognitive misconceptions, 489 respondents’ perceptions of benefits and costs of IoT solutions were analyzed. Using the perspective of ‘‘status quo bias’’, the qualitative analysis reveals that the perceptions of experienced and inexperienced users partly overlap on benefits such as the reduction of errors and relief of personnel. However, the perceptions also diverge in part, as inexperienced users consider IoT solutions to be gimmicky, fostering mistrust. In addition, inexperienced users overestimate learning phases for interacting with IoT solutions, leading to loss aversion and consequently to cognitive misperceptions. Hence, the study examines the gap between experienced and inexperienced users as a neglected aspect in IoT adoption. Further, identifying relevant drivers for the implementation of IoT solutions at the individual level helps to extend the hitherto technical view of IoT solutions towards a multi-layer approach that includes a holistic, behavioral perspective. Keywords Internet of Things Status quo bias  Perception Adoption behavior 1 Introduction The Internet of Things (IoT) represents a cornerstone of the future generation of the Internet and a novel technology paradigm (Atzori et al. 2017; Ben-Daya et al. 2019; Lu and Neng 2010; Mishra et al. 2019). As the adoption of IoT solutions enables the intelligent cross-linking of multiple devices and the real-time collection of information (Mishra et al. 2019; Xia et al. 2012), organizations see opportunities to reduce costs (Sandu and Gide 2017), to improve customer satisfaction (Vermanen and Harkke 2019) and to structure workflows more efficiently (Scuotto et al. 2017). However, at the individual level, IoT solutions are considered to be complex and heterogeneous, requiring specific tools and profound expertise for adoption and maintenance (Mishra et al. 2019). Many employees do not have a clear understanding of the potential of IoT solutions and the extent to which their implementation affects organizational and procedural conditions (Leyer et al. 2017). Previous studies have identified the need to study enterprise-based IoT adoption at the individual level (Hsu and Lin 2016), false perception of IoT solutions (Laumer and Eckhardt 2012), as well as algorithm aversion towards automated information systems (IS) (e.g., Heßler et al. 2022). These misconceptions let employees face IoT solutions reluctantly, leading to slow adoption speed, workarounds and reversions to old working tools and routines (Venkatesh 2006). From a ‘‘status quo bias’’ Accepted after three revisions by Alexander Maedche. M. Rimbeck J. Stumpf-Wollersheim Chair of International Management and Corporate Strategy, Technical University of Freiberg, Freiberg, Germany e-mail: [email protected] J. Stumpf-Wollersheim e-mail: [email protected]e A. Richter (&) School of Business and Government, Victoria University of Wellington, Wellington, New Zealand e-mail: [email protected] 123 Bus Inf Syst Eng 67(6):815–832 (2025) https://doi.org/10.1007/s12599-024-00891-6 perspective, employeespreference to remain in current situations or working practices leads to increased demands on time and costs, along with adverse reactions to the implementation of new information systems (Kim and Kankanhalli 2009; Kim 2011). Previous research concerning usersadoption behavior regarding the IoT primarily focused on IoT services, smart home or healthcare industries (Hsu and Lin 2016; Pal et al. 2018; Williams et al. 2017). Further studies (e.g., Sievers et al. 2021) examined the impact of IoT solutions at the individual and the team level, contributing to an enhanced understanding that IoT-specific attributes may lead to employee empowerment and dynamic team structures. However, thus far, no study has investigated usersnonrational, relative perceptions of costs and benefits as well as potential cognitive misperceptions regarding IoT solutions. Moreover, no study has addressed the differences in perception between experienced users (i.e., employees who have used or are currently using IoT solutions in their working environment) and inexperienced users (i.e., employees who have never used IoT solutions in their working environment). Previous research on inexperienced users explains the reluctance to adopt new information systems with isolation, lack of education, or nostalgic feelings (Kahma and Matschoss 2017). As we assume that inexperienced users tend to stick to their established routines due to cognitive misconceptions, we focus on both experienced and inexperienced users equally. By examining both groups to uncover whether specific benefits or costs are consistently overor underestimated at different levels of experience, we address an important research gap. Specifically, the exploration of employee perceptions offers a highly relevant complement to research that examines adoption behavior at the organizational level for several reasons. First, addressing reactions and feelings at the individual level contributes to psychological identification (Leso et al. 2022). Second, understanding employees’ behavioral intention regarding the implementation of IoT solutions is crucial for the adoption process to proceed as envisioned by management. Third, understanding individual perceptions facilitates the establishment of new routines (Venkatesh 2006). By considering both ends of the experience spectrum, this study aims to provide a comprehensive understanding of the factors influencing IoT adoption, moving beyond a one-size-fits-all perspective. Furthermore, the increasing diffusion of technology as a cultural and organizational phenomenon can be studied more effectively by considering non-use (Satchell and Dourish 2009). Consequently, understanding users’ perception of costs and benefits as well as cognitive misperception means overcoming a fundamental hurdle to achieve greater adoption at the individual level (Boonstra and Broekhuis 2010). We aim to (1) empirically capture IoT users’ non-rational perceptions of costs and benefits of IoT solutions in industry and (2) to identify cognitive misperception and potential differences in perception that may lead to different individual adoption behavior. We surveyed 489 employees about their use of the IoT with primarily openended questions and a focus on IoT solutions in industry, considering both applications in office and production environments. On the basis of the constructs of the status quo bias theory (SQBT), we analyzed the data through an inductive content analysis, allowing for the capture of users’ non-rational perspectives. Within the groups (i.e., experienced and inexperienced users), we also differentiated between employees at the operational and management level to generate in-depth insights related to our research question. Our results, first of all, show that the respondents consider the reduction of errors, improved process management, and the relief of personnel to be the key benefits of IoT adoption. However, while experienced users see the IoT as a long-term challenge to interpersonal exchange, inexperienced users tend to consider IoT solutions as a pointless gimmick that only fuels mistrust. Inexperienced users also overestimate the amount of time it takes to become familiar with IoT solutions. Particularly in this aspect, it is striking that solely employees at the operational level overestimate the familiarization period with IoT solutions. This overestimation is due to the fact that IoT solutions are often seen as complex and nontransparent. Our study provides two highly relevant theoretical contributions: First, based on our empirical qualitative observations, we contribute to an explanation of users preference regarding the integration of IoT solutions in their working routines. In doing so, we contribute to a broader understanding of the implications for the use of IoT solutions (Beck et al. 2022), focusing in particular on non-rational perceptions and potential misconceptions that emerge from algorithmic decision making, along with relevant insights for managers. We analyzed our results through the lens of SQBT according to Samuelson and Zeckhauser (1988) and Kim and Kankanhalli (2009), adding that bridging the gap between experienced and inexperienced users is a neglected aspect in examining IS adoption and user perception. As we identify major differences in perceptions, we further contribute to an improved and in-depth understanding for behavioral attitudes and (negative) adoption decisions towards IoT solutions. Specifically, our research contributes to the validation and extension of existing findings in IS research, especially in relation to SQBT. Our results suggest that uncertainty costs and sunk costs have a significant impact on resistance and, as a result, slower adoption of IS, as previously shown by Hsieh and Lin (2020) and Kim 123 816 M. Rimbeck et al.: Unfolding IoT Adoption: A Status Quo Bias Perspective, Bus Inf Syst Eng 67(6):815–832 (2025) (2011). Moreover, our study indicates that the majority of inexperienced users have no intention to adopt the IoT. In contrast to previous findings suggesting reasons such as isolation, lack of education, or nostalgia (Kahma and Matschoss 2017), we propose that cognitive misconceptions are a central factor leading inexperienced users to stick to their habitual patterns. Second, we determine relevant drivers for the implementation of IoT solutions at the organizational level and thus extend the hitherto technical view of the IoT. Specifically, in reference to well-known multi-layer approaches (e.g., Al-Fuqaha et al. 2015), we extend in particular the application and business layer, by including a holistic, behavioral perspective. Hence, we contribute considerably to a broader conceptual understanding of IoT solutions. Additionally, our study comes along with important practical implications: First, by addressing real-world cases, potential benefits and costs of IoT adoption are particularly emphasized for inexperienced users. Second, both experienced and inexperienced users can benefit from making strategic decisions at different management levels. 2 Theoretical Foundation 2.1 IoT Solutions in Industry and the Impact at the Organizational Level The term ‘‘IoT’’ describes the global interconnection of small independent sensors, complex devices or system environments, representing a generic term for a range of advanced information systems. Linking to the Internet, the devices are able to gather in data from their environment and communicate with other devices (Atzori et al. 2017). Despite different orientations of previous studies (Mihovska and Sarkar 2018), the commonalities converge on the following key attributes: (1) the presence of a specific number of physical objects in the (working) environment, (2) the collection and simultaneous transmission of information to applications for users to access and evaluate, and (3) an enhanced degree of automation regarding human–machine interaction. Unlike user-centric information systems (Butala and Mpofu 2014; Martins et al. 2020), IoT systems communicate directly with each other and autonomously conduct similarly structured routine activities. Accordingly, the IoT provides high volumes of data (Sievers et al. 2021), improving accuracy and efficiency (Luthra and Mangla 2018). In an industrial context, in particular interconnected systems in the production process are receiving increasing attention. For instance, additive manufacturing provides a flexible approach to digitally capture inventory and automatically initiate manufacturing tasks on demand (Haleem and Javaid 2019). In addition, the concept of a smart factory includes real-time monitoring and control of the entire production process through IoT devices, covering production lines, warehouses and distribution hubs (Khan et al. 2020). As a result, fundamental organizational and processrelated changes occur (Brous et al. 2020), which massively interfere with established working routines at the individual level (Ellis and Morris 2015; Hytha et al. 2019; Sherif and Al-Hitmi 2017). Concretely, the management and interpretation of the generated data as new components in established working routines as well as changes in productivity, internal control, self-regulation, and security aspects are becoming increasingly critical at the organizational level (Abera et al. 2016; Chang et al. 2020; Patel and Patel 2016). Due to the object-centric nature of IoT solutions, employees at the individual level are thus involved with process-triggering interactions with objects (e.g., scanning RFID tags) as well as handling interfaces that report the acquired data (e.g., navigating through the system, interpreting data, dealing with visual analytics) (Koren and Klamma 2018). Consequently, a high level of stress and fears to new IoT solutions are expected at the individual level (Reil et al. 2020). 2.2 Behavioral Responses at the Individual Level Considering the three key attributes of the IoT and its deployment in industry, behavioral responses towards IoT solutions from employees can be derived in more depth. First, the presence of a specific number of physical objects massively interferes with existing working routines by capturing a variety of information from the working environment and providing subsequent feedback. In this context, Laumer et al. (2016) indicate that, in addition to the attributes of the IoT itself, the perception of altered working routines induces resistance behaviors. Moreover, IoT objects may be considered as surveillance mechanisms, causing employees to establish invisibility practices toward management (Anteby and Chan 2018). Second, modified or new applications and interfaces, presenting the collected data to users, may reinforce the cognitive loading. Learning and navigating through new interfaces creates changes in the user experience, emerging into a prevalent phenomenon in the industry context, and consequently may lead to resistance behaviors (Hutanu 2021). Third, an enhanced degree of automation may disrupt the psychological balance (Newcomb 1953) of employees. This occurs, for instance, when automated instructions for action (e.g., when errors appear in the production process) conflict with established routines of employees. Consequently, systems are perceived as a threat and trigger resistance (Schein and Rauschnabel 2021). Thus, various forms of resistance behavior are to be expected during the adoption of new IoT 123 M. Rimbeck et al.: Unfolding IoT Adoption: A Status Quo Bias Perspective, Bus Inf Syst Eng 67(6):815–832 (2025) 817 solutions, which is often the case in the context of IS (Basyal and Seo 2017). Resistance behaviors include apathy (e.g., disinterest, inaction), passive resistance (e.g., maintaining established behavior, excuses), active resistance (e.g., expressing dissenting positions, grumbling), or aggressive resistance (e.g., destructive sabotage, threats) (Chreim 2006; Coetsee 1999; Lapointe and Rivard 2005; Laumer et al. 2014). At the beginning of the adoption process, resistance is particularly evident in relation to new IS itself; in later stages of adoption, resistance becomes politicized and tends to target the substance of the IS (Lapointe and Rivard 2005). Critical drivers of resistance include perceived usefulness, ease of use, and threats or risks (Bhattacherjee and Hikmet 2007; Laumer et al. 2016; Maier et al. 2013; Schein and Rauschnabel 2021). It is immensely crucial for companies to detect impelling factors for negative reactions at an early stage, as resistance behaviors are a central reason for not using IS solutions (Basyal and Seo 2017; Laumer and Eckhardt 2012). Further studies emphasize the reluctance of employees to engage with IoT solutions, due to concerns about their inability to adapt and fears of increasing automation and autonomy (Ahmetoglu et al. (2022) and highlight the social dimensions of IoT implementation, citing privacy concerns, surveillance, and distrust as critical challenges that organizations encounter (Birkel and Hartmann 2019). Similarly, de Vass et al. (2021) shed light on the challenges faced by organizations in deploying IoT technologies, including resistance from stakeholders, reluctance to share data, and interoperability issues. Furthermore, Ancarani et al. (2020) contribute insights into the varying degrees of IoT readiness and technological capabilities within organizations. By identifying different clusters of IoT projects, the study emphasizes the diverse nature of IoT solutions and the corresponding impacts on organizational processes and capabilities. In light of these findings, it becomes evident that understanding and addressing employee concerns and resistance behaviors are essential for successful IoT adoption. In addition, studies indicate that risks towards new IS are rated higher by inexperienced users (Schein and Rauschnabel 2021), impeding the individual adoption behavior (Shahbaz et al. 2019). 2.3 Framework for Adoption Behavior at the Individual Level Adoption at the organizational, team, and individual level plays a major role in the field of IS and contains a broad knowledge base, as the process of IS adoption is essential to realize its resulting benefits (Venkatesh 2006; Xia and Lee 2000). For this reason, researchers investigated the adoption process and factors determining adoption decision of IS in organizations to a considerable extent (Hameed and Arachchilage 2020). The examination of individuallevel IS adoption raised several theoretically grounded models, exploring the underlying mechanisms of user adoption behavior. These include the SQBT, which aims to explore non-rational decision making (Samuelson and Zeckhauser 1988) and provides an explanation for employees’ preference to remain in current situations or routines (Kim and Kankanhalli 2009). To address the emergence of the status quo bias, three categories are considered (Kim and Kankanhalli 2009; Samuelson and Zeckhauser 1988). First, rational decision making involves weighing the costs (i.e., transition costs and uncertainty costs) and benefits that arise from deviating from the current status for the end user (Samuelson and Zeckhauser 1988). Second, cognitive misperception of potential losses leads to a stronger perception of potential (insubstantial) losses caused by deviating from the current status than potential benefits, thus creating a bias (Kahneman and Tversky 1979; Novemsky and Kahneman 2005). Third, sunk costs relative to prior commitments, social norms toward change in the working environment, and efforts to maintain a feeling of control create a psychological commitment toward the change (Samuelson and Zeckhauser 1988). The perceived value of the change is determined by switching benefits (i.e., increase in outcome while decrease in input) and switching drawbacks (i.e., increase in input while decrease in outcome) (Kim and Kankanhalli 2009). Accordingly, the categories incorporate not merely rational criteria, which primarily include a price dimension, but also non-rational aspects at the psychological level, which cause a status quo bias. The SQBT is used in IS research and is particularly suitable for investigating mandatory IS implementation (i.e., decisions made at management level). In this regard, studies reveal that uncertainty costs, sunk costs, and perceived value of IS-based change have a significant impact on user resistance (Hsieh and Lin 2020; Kim 2011). In addition, transition costs and perceived loss negatively affect perceived value and thus indirectly impact user resistance to change (Kim 2011). Transition costs and perceived sunk costs, along with the associated habitual use of familiar systems, also reinforce inertia to adopt new IS (Polites and Karahanna 2012; Shankar and Nigam 2022). Accordingly, status quo bias significantly influences user resistance and slower adoption to change, which are reflected, for example, in grumbling among employees (Alzahrani et al. 2021), conflict, and increased resource consumption (Kim 2011). Despite the rich abundance of research on IS adoption at the individual level through the lens of SQBT, some aspects have remained largely unconsidered so far. First, it requires further research that considers the specific nature 123 818 M. Rimbeck et al.: Unfolding IoT Adoption: A Status Quo Bias Perspective, Bus Inf Syst Eng 67(6):815–832 (2025) of IS and its associated attributes to generate an in-depth understanding of users and their behavior (Venkatesh 2006). Second, in spite of comprehensive, empirical results on the individual-level adoption of IS-users, the perception of inexperienced users received little consideration (Jahanmir and Cavadas 2018; Satchell and Dourish 2009). One exception is a study that indicates that a decrease in skepticism and an improvement in the positive perception of potential consumers towards IS leads to accelerated adoption and subsequent diffusion (Jahanmir and Cavadas 2018). From a user engagement perspective, Melby et al. (2016) also suggest that it is important to consider that not all users are equally engaged and willing to adopt new IS according to management specifications. Moreover, IS users should be viewed in conjunction with inexperienced users to create sensitivity to the reasons for non-use (e.g., skepticism, fears) and to promote a differentiated understanding of existing concepts (Wyatt 2014; Wyatt et al. 2002). The perception both of experienced users and inexperienced users is thus valuable to encourage the adoption of IS at the individual level. Third, to investigate and extend the SQBT, researchers primarily use quantitative approaches, which provide highly relevant results. However, qualitative research allows to closely examine the specific type of IS, here IoT solutions, and the actual deployment (Vogelsang et al. 2013) in industry. Accordingly, qualitative approaches might contribute to an indepth understanding of perceived costs and benefits of IoT solutions as well as a differentiated consideration of experienced and inexperienced users. Due to its non-rational perspective, the SQBT seems appropriate as an underlying basis for qualitative research designs. Figure 1shows the adaptation to our qualitative approach. The gray arrows indicate that we do not examine the already very well-studied relationships between the single constructs, but conduct an in-depth analysis of the relevant factors for perceived benefits, and costs as well as psychological commitment and cognitive misperception of IoT solutions. Accordingly, we understand that an adoption decision at the individual level is a longer-term effort that requires meaning and value of IS to be identified. We are confident that our analysis, clustered by using the SQBT, will contribute to a rich understanding of IoT implementation. 3 Method 3.1 Survey Design To investigate perceived benefits, and costs as well as psychological commitment and cognitive misperception of IoT solutions, depending on users’ experience, we surveyed 489 employees from different industries and company sizes from June to July 2020 on their user perception of IoT solutions. To gain deeper insights into the perception of IoT solutions by experienced and inexperienced users, a questionnaire with mainly open questions was drawn up. In line with Kim and Kankanhalli (2009), we operationalized rational decision making by net benefits (i.e., increasing effectiveness and efficiency of using new IS), transition costs (i.e., learning costs, permanent costs), and uncertainty costs (i.e., perception of risks, psychological uncertainty). Psychological commitment is operationalized by sunk costs (i.e., abilities related to previous working routines), and efforts to feel in control (i.e., fear of losing control when using new IS). Cognitive misperception is operationalized trough loss aversion (i.e., higher weighting of losses than gains). Furthermore, we operationalized attitude towards using IoT solutions by perceived value (i.e., an overall evaluation by comparing benefits and costs) (Kim 2011). In the following, we will Fig. 1 Adapted SQB approach for qualitative research design 123 M. Rimbeck et al.: Unfolding IoT Adoption: A Status Quo Bias Perspective, Bus Inf Syst Eng 67(6):815–832 (2025) 819 group the categories under the terms ‘‘benefits’’ and ‘‘costs’’ for ease of understanding. We would like to emphasize that we include both the rational and non-rational aspects that may cause a status quo bias under the two terms. First, to query whether IoT solutions are currently used by the respondents, we used a 5-point Likert scale to determine the degree to which they define working devices as IoT-objects, i.e., objects that are equipped with sensor technology and intelligently connected (1 = no objects of this type are intelligently connected; 5 = all objects of this type are intelligently connected). We provided the respondents with a pre-defined list of IoT solutions typical for industry to select from (see Appendix 1; available online via http://link.springer.com). We identified the single items (e.g., office desk, printer, garbage can) and interconnected systems (e.g., building infrastructure, machines, production objects) based on a literature review that revealed the most commonly used IoT solutions in an industrial context. According to the question of use, we divided the participants into experienced users and inexperienced users: In the case that participants stated that none of the objects in their working environment was intelligently connected, they were identified as an inexperienced user. In the case that participants stated at least once that they use these items, they were identified as an experienced user. Incorrect assumptions about their IoT use were avoided by asking about the interconnection of working devices in their immediate working environment, which can be well evaluated by the respondents. Inexperienced users were then asked open questions about the intended use, potential areas of application as well as perceived benefits, and costs (see Appendix 2, Questions 1–5). Simultaneously, we asked experienced users about the duration of use, existing areas of use as well as benefits, and costs (Questions 6–9). Here we have adapted the wording of the questions slightly. We decided to formulate the questions in an open manner in order to obtain a wide range of diverse, unbiased answers. Fourth, we asked the respondents personal questions about the industry, companies, and tasks. 3.2 Sample and Data Collection To ensure that only persons participate, who can evaluate the presence (or absence) of IoT solutions in an organizational context, we excluded non-employees from the further survey prior to data analysis (n= 74). The final target population of the survey included employees of organizations in Germany (N= 489), including managers (n= 155) and employees without a manager position (n= 334). Overall, the proportion of female (n= 266) and male participants (n= 219) was relatively equal, with an average age of 45.90 years (SD = 11.24). We recruited participants through an access panel, which was provided by consumer fieldwork. The respondents covered various functional areas: A high proportion of respondents were involved in primary activities, with persons from service (n= 67) as well as marketing and sales (n= 58) being particularly prominent as compared to employees from operations (n= 30) and logistics (n= 19). Employees from secondary activities were mainly engaged in firm infrastructure and management (n= 64) and technology development (n= 35). Persons from the functional areas of procurement (n= 12) and human resources management (n= 14) were barely represented. In addition, there was a relatively high number of respondents classified in the category ‘‘other’’ (n= 132). Of those surveyed, more than half worked in small and medium-sized enterprises (n= 275); 42.90%, however, in companies with more than 250 employees (n= 210). The companies have been assigned to different industries. In reference to the Global Industry Classification Standard, depicting 11 sectors, mainly employees from industrials (n= 134) and consumer discretionary (n= 142) were included. Besides medium (e.g., service, n= 69; health and social care, n= 47; financials, n= 26; information technology, n= 15) and low represented sectors, a relatively high number of participants assigned to ‘other’ (n= 102). Tables 1,2, and 3indicate the descriptive statistics in detail. 3.3 Coding Procedure In qualitative research, coding refers to the process of organizing and categorizing data to identify patterns, themes, and concepts (Mayring et al. 2004). Thus, coding allows for the identification of similarities and differences within the data set and helps to structure the data in a way that facilitates analyses and interpretation. We used MAXQDA (version 18.2.4) to conduct a qualitative content analysis (Mayring et al. 2004). Using the program, we calculated the Brennan-Prediger coefficient (Brennan and Prediger 1981) since this predictor is robust to the frequency distribution of the codes to be rated (Quarfoot and Levine 2016). To ensure an objective coding of the participants’ answers, we followed a stepwise inductive procedure. After we separated the answers depending on the users’ experience with IoT solutions, we identified perceived benefits, costs, and psychological commitment for the adoption of IoT solutions. From the aggregated results, we determined subsequent cognitive misperception and attitude towards using. First, for the development of the coding scheme ‘differences in IoT perception’, the first author of the article and one additional coder independently analyzed the questionnaires and coded the open questions (Questions 4–5; 8– 123 820 M. Rimbeck et al.: Unfolding IoT Adoption: A Status Quo Bias Perspective, Bus Inf Syst Eng 67(6):815–832 (2025) 9; see Table 4). Differences in coding were then discussed, and an initial coding scheme was created. Second, we passed the coding scheme as well as coding descriptions and guidelines on to two subsequent coders, who re-analyzed the open questions. Due to the insufficient intercoder agreement in some of the categories (r_min = 0.20, r_max = 0.72), we modified the coding scheme for these particular categories. Concretely, we combined some categories to a more aggregated level. For example, when benefits were reviewed, the categories ‘improved information flow’ and ‘location and time-independent data access’ were summarized into a new category. In addition, we added the previously separately listed category ‘increased efficiency and effectiveness’ and ‘time recording and time savings’ to the category ‘process management’. Claims that there is no understanding of benefits or costs were each assigned an additional category. Although this category is not valuable in terms of content, it serves the purpose of completeness. Based on these revisions, we created a final coding scheme containing 11 categories for experienced users (seven for net benefits, two for costs, and two for psychological commitment) and 15 categories for inexperienced users of IoT solutions (two additional categories for assumed net benefits, one additional category for assumed costs, one additional category for assumed psychological commitment). The resulting intercoder agreement in the third round was higher than 0.75 for all codes (r_min = 0.89, r_max = 0.99, M = 0.95, SD = 0.04), which is tolerable for an inductive content analysis (Landis and Koch 1977). In Table 4, the final coding scheme for differences in IoT perception is shown, which includes the code descriptions, the coding frequencies (n), and the intercoder agreement (r). The coding frequency represents the number of codings that occurred among all participants. The lowered numbers indicate the corresponding groups; i.e., 1 represents experienced users and 2 represents inexperienced users. To examine perceptions with regard to IoT solutions in further detail, we compared perceptions at the management and the operational level in the next step. For this purpose, we calculated the relative codings for participants with and without management responsibility in relation to the absolute number of codings. The letters in brackets indicate the operational level (o) and the management level (m). 4 Findings The results are structured as follows: First, we analyze net benefits, costs, and psychological commitment of IoT adoption, differentiated into experienced users (k1) and inexperienced users (k2), considering operational and management level. Second, we examine cognitive misperception and attitude towards using. 4.1 Net Benefits In total, we identified 595 codes for net benefits of IoT adoption. Useful factors that were equally described by experienced and inexperienced users can be classified into seven categories, specifically (1) functionality and error reduction (k1 = 109, k2 = 56), (2) process management Table 1 Frequencies and distributions Np Gender Male 219 44.8 Female 266 54.4 No answer 4 0.8 Level of responsibility Management level 155 31.7 Operational level 334 69.3 Area of expertise Service 67 13.7 Marketing and sales 58 11.9 Operations 30 6.1 Logistics 19 3.9 Firm infrastructure 12 2.5 Management 52 10.6 Technology development 35 7.2 Procurement 12 2.5 HR management 14 2.9 Other 132 27.0 Company size B9 90 18.4 B49 81 16.6 B249 104 21.3 250 and more 210 42.9 Internationality International 179 36.6 Domestic 305 62.4 No answer 5 1.0 Table 2 Descriptive statistics N Min Max M SD Age 487 18 68 45.90 11.24 Duration in company 451 1 45 11.67 9.53 Duration in working environment 447 1 47 14.68 10.26 123 M. Rimbeck et al.: Unfolding IoT Adoption: A Status Quo Bias Perspective, Bus Inf Syst Eng 67(6):815–832 (2025) 821 (k1 = 89, k2 = 77), (3) relief of personnel (k1 = 69, k2 = 35), (4) networking and data (k1 = 36, k2 = 19), (5) cost reduction (k1 = 22, k2 = 22), (6) resources and sustainability (k1 = 21, k2 = 16), and (7) modernity (k1 = 10, k2 = 14). Comparing the two groups, it is remarkable that the expectations of inexperienced users correspond largely with the perception of IoT experienced users. However, it is apparent that the perceptions of employees at the operational and management levels differ considerably in some categories. Regarding the most mentioned category (1) functionality and error reduction, respondents stated that the implementation of IoT solutions leads to a reduction in failures, as IoT systems provide automatic solutions, especially for orders, re-orders, and schedules (see Table 5). By eliminating the necessity of manual resubmissions, errors are avoided at an early stage, thus reducing the error rate. In this context, IoT systems are considered to be transparent, accurate, functional and reliable. While these benefits are perceived to be relatively similar in strength by IoT-experienced employees at the operational and management levels, inexperienced employees at the management level in particular are much less likely to perceive the opportunity to increase functionality and reduce errors, potentially leading to a reduced willingness to implement IoT solutions in their departments. In category (2) process management, respondents were positively impressed by how workflows run faster, more effectively and without idle time, bottlenecks and waiting times as a result of adopting the IoT. In addition, IoT solutions enable a more flexible and faster response to changing requirements, which results in improved coordination and delivery procedures. A similar pattern emerges here as in the previous category, namely that IoT inexperienced managers may underestimate the potential with regard to process management. In addition to the two most frequently mentioned categories, the (3) relief of personnel is named as a positive aspect regarding the net benefits of IoT solutions. In particular, the elimination of routine activities, which are often perceived as time-consuming and burdensome, is commonly reported. Furthermore, the respondents indicate that fewer routine activities and fewer work interruptions, reduces effort and allow them to concentrate on essential work content. Again, perceptions in this category are relatively similar among the subgroups of experienced users, whereas inexperienced users at the management level have a substantially weaker perception of the impact of IoT solutions on relieving personnel. Since this group of employees makes strategic decisions relating to the implementation of IoT solutions, these results must be considered rather critical (Tables 6,7,8). 4.2 Transition and Uncertainty Costs Regarding the transition and uncertainty costs, a total of 286 statements were coded and allocated to the following categories: (1) employees and emotions (k1 = 96, k2 = 47), and (2) financial and personnel expenses (k1 = 85, k2 = 58). We identified similarities between experienced and inexperienced users in (2) financial and personnel expenses, representing transition costs. Financial efforts arise in particular from the acquisition, continuous service and data maintenance, support services and contractual obligations. From the perspective of the inexperienced users, personnel expenditure is caused in particular Table 3 Differentiation between experienced and inexperienced users according to the questionnaire Experienced users Inexperienced users np n p 1 Office desk/chair (e.g., automatic adjustment to user data) 62 12.7 424 86.7 2 Printer (e.g., automatic reordering when toner is empty) 203 41.5 281 57.5 3 Trash can (e.g., disposal order, when it is full) 45 9.2 439 89.8 4 Filing systems (e.g., reminder if something has been stored for too long) 90 18.4 396 81.0 5 Robots (e.g., vacuum cleaner robots, logistics robots) 88 18.0 394 80.6 6 Key fobs (e.g., automatic working time recording) 118 24.1 368 75.3 7 Building infrastructure (e.g., automatic blinds, thermostats) 181 36.9 305 62.4 8 Machines (e.g., automatic maintenance, order forwarding) 143 29.3 344 70.3 9 Production objects (e.g., automatic status notification, commissioning of activities) 127 25.9 359 73.4 10 Other objects 97 19.8 387 79.1 Participants had the option to select only those IoT solution options that applied to them. Therefore, the distribution of responses across the options varies; the division between experienced and inexperienced users was the primary criterion for subsequent data evaluation 123 822 M. Rimbeck et al.: Unfolding IoT Adoption: A Status Quo Bias Perspective, Bus Inf Syst Eng 67(6):815–832 (2025) quantitative (experimental) data to investigate both perception and current adoption behavior in order to make causal predictions about whether a positive (negative) perception of the IoT leads to a positive (negative) adoption decision, considering the distinction between experienced and inexperienced users. Based on the empirical results from various IoT industries, our study confirms that the perception of net benefits, costs, and psychological commitment are relevant when implementing IoT solutions. Furthermore, with our methodological design, we cannot fully exclude that respondents might not have been aware of their use of IoT devices in their working environment and, accordingly, may have inadvertently delivered incorrect responses. However, based on an observation from a pretest suggesting that a considerable proportion of participants were unaware of the IoT concept, we decided to ask indirectly about experience with IoT solutions in order to prevent respondents from unintentionally giving incorrect answers. A plausible explanation for this observation might be that the survey focused on German employees and that IoT implementation in companies in Germany is relatively low. Accordingly, future research may use experimental designs to provide distinct inferences regarding the impact of varying levels of the deployment of the IoT. Third, more research is required to investigate the relationship between IoT adoption and the resulting consequences at the organizational, team, and individual level. For instance, to what extent the increasing diffusion of IoT solutions fosters changes in the fluidity of teams and what impacts are to be anticipated at the organizational and at the team level should be subject to future research. In this context, it is crucial to consider that the positive or negative consequences of implementing IoT solutions depend largely on various parameters and thus cannot be generalized for all types of organizations. These parameters include, in particular, financial and personnel aspects, covering procurement, commissioning and maintenance as well as consulting services by internal or external experts. Accordingly, future research might consider conducting case studies in companies of different sizes and industries to analyze how different economic factors influence the decision-making process and outcomes of IoT adoption. In addition, frameworks for performing comprehensive cost–benefit analyses specific to IoT adoption can be developed to help organizations evaluate the tangible and intangible costs and benefits associated with integrating IoT into their operations. 6 Conclusion In our study, we highlighted the relevance of considering user perceptions through the lens of SQBT and revealed how IoT solutions are perceived by experienced and inexperienced users. In addition to the similarities and differences, we also found that IoT solutions may change team structures and lead to so-called fluid teams. In general, the compulsive increase in flexible working forms is forcing a growing number of managers to allocate personnel flexibly and to exploit the increasing level of automation, virtual platforms and data analysis to a greater extent. To remain competitive in both the ongoing digitalization process and the prevailing uncertain market situation, it is even more important to overcome misconceptions, reduce resistance and adopt IoT in the long term. Supplementary InformationThe online version contains supplementary material available at https://doi.org/10.1007/s12599024-00891-6. Acknowledgements This work was supported by the German Federal Ministry of Education and Research and the European Social Fund [grant number 02L18B030ff]. Funding Open Access funding enabled and organized by CAUL and its Member Institutions. 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