Still doing it yourself? Investigating determinants for the adoption of intelligent process automation

Mayr, Alexander; Stahmann, Philip; Nebel, Maximilian; Janiesch, Christian Article — Published Version Still doing it yourself? Investigating determinants for the adoption of intelligent process automation Electronic Markets Provided in Cooperation with: Springer Nature Suggested Citation: Mayr, Alexander; Stahmann, Philip; Nebel, Maximilian; Janiesch, Christian (2024) : Still doing it yourself? Investigating determinants for the adoption of intelligent process automation, Electronic Markets, ISSN 1422-8890, Springer, Berlin, Heidelberg, Vol. 34, Iss. 1, https://doi.org/10.1007/s12525-024-00737-9 This Version is available at: https://hdl.handle.net/10419/315703 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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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/ Vol.:(0123456789) Electronic Markets (2024) 34:56 https://doi.org/10.1007/s12525-024-00737-9 RESEARCH PAPER Still doing it yourself? Investigating determinants fortheadoption ofintelligent process automation AlexanderMayr1· PhilipStahmann2 · MaximilianNebel2· ChristianJaniesch2 Received: 23 April 2024 / Accepted: 14 October 2024 / Published online: 14 November 2024 © The Author(s) 2024 Abstract Intelligent process automation (IPA) augments symbolic process automation using artificial intelligence. Emulating human decision-making, IPA enables the execution of complex processes requiring decision-making capacities. IPA promises great economic potential as it enables more efficient use of the human workforce. However, the adoption rate in practice falls behind these potentials. Our study aims to investigate reasons and identify areas for action towards IPA adoption. To this end, we identified 13 determinants and created an extended UTAUT model. We tested the model with partial least squares structural equation modeling for significant influential relationships between the determinants based on a user study. We contribute to theory and practice finding a special role of trust and transparency for the adoption of IPA. Likewise, we show that organizations should cultivate a positive attitude towards IPA diffusion. Further, our results contribute with a focus on the potential adopters as IPA adoption is contingent upon their characteristics, such as experience and job level. Keywords Intelligent process automation· Business process management· Robotic process automation· UTAUT · Technology adoption JEL Classification C9· M15 Introduction The idea of automation has characterized efficient work design for decades. Along with technological advancements, tasks originally performed by humans have been delegated to new technology (Rinta-Kahila etal., 2023). For example, machines have been designed to automate repetitive manufacturing tasks or office work. Delegation to technology has leveraged two kinds of advantages. On the one hand, workers’ capacities that were invested in repetitive tasks could be used otherwise. On the other hand, automation streamlined task execution leading to reductions in operational failures and manufacturing variations. Opposed to physical labor, knowledge-intensive tasks have remained mostly untouched by automation as they required human cognition for decision-making (Rinta-Kahila etal., 2023). However, the pressure to automate knowledge-intensive tasks grows as the work amount in front and back offices increases every year binding more and more capacity (Willcocks, 2020). To leverage back office and front office automation potentials in the past, organizations have used symbolic process automation enabled by business process management Responsible Editor: Luba Torlina A prior version of this research has been published here: https:// aisel. aisnet. org/ icis2 023/ itado pt/ itado pt/6/. We significantly extended the prior publication throughout all sections, but especially in the methodology, result presentation, and discussion. The authors have the legal rights for further publications. * Philip Stahmann [email protected] Alexander Mayr [email protected] Maximilian Nebel [email protected] Christian Janiesch [email protected] 1 Paxray GmbH, Gmünder Str. 14, 73557Mutlangen, Germany 2 Chair ofEnterprise Computing, TU Dortmund University, Otto-Hahn-Str. 12, 44227Dortmund, Germany Electronic Markets (2024) 34:5656 Page 2 of 22 (BPM) systems and robotic process automation (RPA) software (Herm etal., 2021) to automate highly standardized and transaction-intensive processes (Asatiani & Penttinen, 2016; Fersht & Slaby, 2012). As symbolic process automation necessitates the explicit formulation of sequence flows and rules, it cannot be used for a significant portion of business processes that require cognitive efforts, such as complex decision-making or judgment (Chakraborti etal., 2020). Intelligent (process) automation (IPA) complements symbolic process automation with artificial intelligence (AI) technology, which mimics human cognitive abilities for decision-making (Engel etal., 2023; Janiesch etal., 2021). Enhanced by AI, the IPA toolbox spawns promising opportunities to automate complex processes that require cognition and had to be performed by human agents until recently. IPA may be useful in tackling sophisticated process steps such as evaluation, reasoning, decision-making, and process fulfillment (Chakraborti etal., 2020; IEEE, 2017). IPA can automate complex tasks such as image and natural language processing, optical character recognition, prediction, or reasoning and consequently increases efficiency and result quality (Herm etal., 2021). Although IPA can represent an essential aspect for organizations to ensure their relevance and competitiveness, many organizations are not implementing these solutions on a large scale (Jyoti & Szurley, 2021). The low adoption of technologies in general can intuitively be broken down to inhibited successful implementations in individual organizations, which in turn has been shown to be highly dependent on individual employee adoption of technologies (Venkatesh & Bala, 2008). This raises the question which factors determine successful embedding of IPA in organizations (Engel etal., 2022) and, hence, adoption by employees. To investigate the determinants and further identify implications that are likely to increase the adoption rate of IPA, we formulate the following research question: RQ: Which determinants influence the adoption of intelligent process automation by employees? Providing answers to this research question, we respond to the call for research by Engel etal. (2022), who observe a low adoption rate of IPA in business organization despite their awareness of its great potentials. Specifically, the call addresses leveraging work system-oriented research opportunities regarding a socio-technical understanding of how to embed IPA in organizations. With our research, we identified determinants for IPA adoption from literature and practice and extended the established Unified Theory of Acceptance and Use of Technology (UTAUT) model accordingly. Our contribution focuses an extension of the established UTAUT model specifically for IPA adoption. We evaluated the extended model in an iterative manner. Our results show that in addition to established factors for technology adoption, trust, transparency, and attitude towards technology are primary decision factors. Therefore, we argue for the cultivation of a positive attitude towards IPA and the establishment of facilitating conditions for its use. In a similar vein, based on our study results, we emphasize the influence of user experience as well as trust facilitated by transparency on IPA adoption. The remainder of this paper is structured as follows: “Theoretical background” outlines the theoretical background on process automation and the adoption of IPA. “Research design” covers the research design. Subsequently, “Derivation of determinants and hypotheses” details the derivation of potential determinants for adoption. “Model evaluation” presents the evaluation of the model, “Hypothesis evaluation” the evaluation of the hypotheses. “Discussion” includes a discussion, implications for theory and practice, and limitations. Lastly, in “Conclusion and future work,” we draw a conclusion and provide starting points for future research. Theoretical background Symbolic andintelligent automation ofprocesses (Knowledge) Work is usually organized in interrelated processes comprising events, tasks, and decision points. Involved actors interact with physical or intangible objects to pursue business goals typically comprising quantifiable value. Using traditional process automation means such as business process management (BPM) systems or robotic process automation (RPA), the sequence of the tasks is determined by handcrafted process models. Decision gateways enable variants in execution (Dumas etal., 2018). Processes can be differentiated by many means. One example is frequency and variance of tasks (van der Aalst etal., 2018). Traditionally, processes that are of high frequency and only exhibit reasonable variance are automated by heavyweight BPM systems as workflows. These implementations rely on handcrafted process models, interfaces to BPM software and often involve multiple departments within or across companies. Processes that involve highly repetitive tasks but do not have a frequency and feasibility high enough for heavyweight automation are—of recently— candidates for lightweight automation with RPA. RPA is a generic term that summarizes a large number of different automation approaches. They have the common characteristic of performing digital, yet manual activities without changing existing software by instantiating software robots as agents that imitate human users instead (van der Aalst etal., 2018). These software robots act on the user interface (UI) and do not intervene into application code (Agostinelli etal., 2019) as suitable interfaces other than the UI often do not exist. RPA use intends to remove labor-intensive, repetitive tasks from the workload of human workers (Chakraborti Electronic Markets (2024) 34:56 Page 3 of 22 56 etal., 2020). Processes that are typically prone to automation with RPA are characterized by a high degree of standardization, no or few exceptions, the divisibility into simple and unambiguous rules, a sufficiently large volume of transactions, and low or no interaction with human workers (Asatiani & Penttinen, 2016; Fersht & Slaby, 2012). Moving, pasting, copying, unpacking, and merging data between systems are typical examples (Aguirre & Rodriguez, 2017). As with BPM systems, RPA requires implementation in a symbolic manner by formulating explicit sequence flows and decision rules (Asatiani & Penttinen, 2016; Fersht & Slaby, 2012). Both approaches can be summarized under the term symbolic process automation (Herm etal., 2021). However, a significant portion of business processes cannot be automated in this manner as they require cognitive capacities (Chakraborti etal., 2020). IPA subsumes approaches that potentially overcome the limitations of symbolic process automation (Engel etal., 2022). IPA represents an approach that complements and augments the methods of symbolic process automation with the benefits of AI. Enhancing process automation with AI based on machine learning entails a significant shift from deterministic rulebased to probabilistic learning-based logic (Engel etal., 2023). As machine learning leverages various kinds of statistical methods and is used for a variety of purposes, there are multiple facets to its definition (Russell & Norvig, 2021). Regarding process automation, AI based on machine learning contributes with capabilities of autonomous, selfadapting decision-making behavior (Engel etal., 2022). AI decision-making is inspired by biological cognition as AI attempts to emulate human intelligence (Janiesch etal., 2021). Combined with advancements in computing power, AI constitutes a strong accelerator for process automation as it comprises complex probabilistic models enabling reflected, adapting decision-making (Dalzochio etal., 2020). IPA therefore holds potential to automate complex processes and tasks that otherwise must be completed by humans. Processes that can potentially benefit from these abilities typically comprise a large number of decision variables, from simple tasks such as invoice verification to complex tasks such as enabling sharing data within data trust models. More generally, IPA bears potential for tasks covering evaluations, reasoning, decision-making, and process fulfillment of deterministic and probabilistic nature (Chakraborti etal., 2020; IEEE, 2017). Therefore, IPA can significantly contribute to strategic business transformation by leveraging operational efficiency (Lacity etal., 2021). Adoption ofintelligent process automation andtheories ofacceptance anduse Despite the potential to gain a competitive edge, companies are hesitant when it comes to IPA adoption. Reports on realizing advantages due to IPA have prognostic character, but do not reflect operational practice (Lacity etal., 2018). Only about one quarter of early technology adopters have implemented IPA (Lacity etal., 2021). Hesitation is due to a variety of risks that specifically affect knowledge workers on operational level. Exemplary risks are disclosed advantages, lack of communication, estimated complexity of IPA adoption, insufficient change management, and fear of being replaced by technology (Engel etal., 2023). However, literature on determinants for IPA adoption is scarce, while calling for more actionable research on IPA adoption prevail (Engel etal., 2022; e.g., Engel etal., 2023). When it comes to the adoption of novel technology, Information Systems research typically draws on established models to investigate adoption determinants and their relationships. UTAUT models are primarily evaluated in research using structural equation modeling (SEM) (Williams etal., 2015). Structural equation modeling (SEM) aims to depict theoretically or logically founded relationships between latent constructs in a system of equations. The method can be used to estimate dependencies and errors between the defined constructs (Weiber & Mühlhaus, 2014). The suitability of UTAUT was proven in different contexts of technology acceptance (Hsu etal., 2014). For example, over 70 percent of the variance of the corresponding target variables could be explained in a large number of studies (Sohn & Kwon, 2020). Besides UTAUT, further theories exist that cater for similar yet slightly different contexts. The Technology Acceptance Model (TAM) aims at understanding acceptance and adoption of new technologies (Venkatesh & Bala, 2008). Additional to behavioral intention and use behavior, perceived usefulness and perceived ease of use are the constructs at its core, which are altered by various determinants such as social influence. Furthermore, the Theory of Planned Behavior conceptualizes a user’s intention of performing a behavior with an information system as determined by their attitude, subjective norms as well as perceived behavioral control (Ajzen, 1991). The Social Cognitive Theory targets fostering an understanding of how observation, imitation, and reinforcement in social environments influence cognitive processes during technology adoption (D. Compeau etal., 1999). Comparably, the Theory of Reasoned Action considers users’ behavioral intention the key determinant of their actual behavior (Sheppard etal., 1988). While the original theory does not explicitly refer to technology adoption, insights, for example on the role of subjective norms, have been used in Information Systems research in this regard (e.g., Albayati etal., 2020; Jain etal., 2022). Moreover, the Motivational Model sets apart by scrutinizing individual intrinsic and motivators which lead to different levels of technology engagement (Vallerand, 1997). The individual motivational model of users is conceptualized to be a major Electronic Markets (2024) 34:5656 Page 4 of 22 determinant of technology adoption. In addition, the Innovation Diffusion Theory characterizes a process for the adoption of new technologies by users (Moore & Benbasat, 1991). In this regard, technology adoption follows a predictable pattern differentiating, for example, innovative users adopting new technologies from more traditional laggards. Pursuing our research goal, we decided to use an established model, either UTAUT or TAM. This allows us to stand on the shoulders of those who introduced and evaluated the original as well as further variables and items in this context. This enables the comparison of our results with prior and future research and allows us to draw broader conclusions taking into consideration the results of others as well. Developing our own model would have increased the complexity of our investigation and would have made this comparability of results more difficult. The risk of establishing YAMA, yet another modeling approach, is something we strived to avoid (Oei etal., 1992). Aligning with Venkatesh (2022), who explicitly proposed the use of UTAUT to investigate acceptance of AI-related technology, we decided for UTAUT. Furthermore, UTAUT is often extended in literature to include specific constructs to customize to specific adoption contexts (Chatterjee & Bhattacharjee, 2020; Venkatesh, 2022; Williams etal., 2015). As exemplary extension, Venkatesh etal. (2012) developed UTAUT2 confirming the structure of UTAUT, but additionally covering Hedonic Motivation and Price Value. In addition, we argue that business processes may constitute a complex field for automation (e.g., Engel etal., 2022). To grasp the complexity from an acceptance perspective, we selected UTAUT due to the variety of considered constructs originating from a variety of established technology acceptance models (Ajzen, 1991; Bandura, 1986; D. R. Compeau & Higgins, 1995; Davis, 1985; cf. Davis, 1989; Davis etal., 1989, 1992; Moore & Benbasat, 1991, 1996; Sheppard etal., 1988; Taylor & Todd, 1995; Thompson etal., 1991; Triandis, 1977; Vallerand, 1997; Venkatesh etal., 2003; Venkatesh & Speier, 1999). This foundation is critical to understanding technology acceptance in the focused organizational setting. As we are among the first ones with our focus of investigation, we wanted to incorporate a comprehensive spectrum of meaningful to get a broad understanding of acceptance of IPA. Research design The goal of our research is to identify determinants for IPA adoption. Using the determinants, we aim for an extension of the UTAUT model that contributes to the research domains of technology acceptance and process automation. Figure1 shows the methodologies we used and how we related them in correspondence to related research (cf. Sumak etal., 2010; Wanner etal., 2022). The methodological procedure comprises six steps as outlined in the following. The steps either focus on theory building or evaluation. (a) To understand the theoretical basis and for the initial identification of determinants for IPA adoption, we conducted a structured literature review (vom Brocke etal., 2009; vom Brocke etal., 2015). We used the five databases ACM Digital Library, AISeL, EBSCOhost Business Source Premier, IEEEXplore, and Web of Science. The choice of databases was due to their coverage of high-quality outlets of related research from Information Systems. We used the search term ((“unified theory of acceptance and use of technology” OR utaut OR “technology acceptance model” OR “theory of planned behavior” OR “social cognitive theory” OR “theory of reasoned action” OR “motivational model” OR “Innovation diffusion theory”) AND (“business Fig. 1 Research procedure Electronic Markets (2024) 34:56 Page 5 of 22 56 process management” OR “intelligent automation” OR “process automation” OR “artificial intelligence”)). The first part of the search term refers to UTAUT as well as related theories comprising potential determinants relevant for extending UTAUT. The second part of the search term broadly covers terms relating to the (intelligent) automation of business processes. We considered scientific journals and conference proceedings. Initially, we identified 2441 publications. After removing duplicates and scanning abstracts and keywords, we reduced the corpus to 152 publications. In a full text analysis, we classified 67 papers as relevant to our research goal. During scanning and full text analysis we excluded all publications that (1) did not refer to the intelligent automation of processes or (2) did not contribute to the identification of adoption determinants, for example, as they were purely theoretical. Publications covering models on technology acceptance were omitted from the first exclusion criterion to enable incorporating a broad perspective on acceptance. Subsequently, we performed a forward and a backward search and identified 73 publications resulting in 225 publications overall. Of these, 79 contain specific research models in the context of technology acceptance. The remaining publications include general as well as specific research directly or indirectly related to IPA. For the synthesis of the publications, we created a concept matrix (Webster & Watson, 2002). (b) We assessed the identified determinants with four interviews with practitioners that engage with IPA. We decided on a two-part interview structure. In the first part, we asked the interviewees for personal attributes such as their organizational role, focus of expertise, and years of experience. After that, the interviewees were asked to quantify their degree of familiarity in the areas of IPA, (symbolic) process automation as well as AI on a 5-point Likert scale. Subsequently, we provided the interviewees with the derived potential determinants for adoption. The interviewees were asked to quantify the perceived relevance of the constructs on a 5-point Likert scale of increasing relevance to enhance comparability. In the second part, there was an isolated free discussion of the interviewees’ perceived relevance of the identified determinants. The interviews were recorded and transcribed in a denaturalized manner (Azevedo etal., 2017). The total duration of the interviews was 172min. All dialogues were transcribed to 6,736 words. (c, d) Subsequently, we related the evaluated determinants formulating hypotheses (see Section Derivation of Determinants and Hypotheses). The hypotheses were formulated analogously to those of UTAUT and relatable models (cf. Appendix 4). Special consideration was given to ensuring that each hypothesis could be evaluated using quantitative measurements established in the literature in the form of questionnaire items. (e) We evaluated the hypotheses in a preliminary online survey using prolific.com for participant acquisition. Participants were presented with a summary of IPA and the technologies it incorporates, as well as a hypothetical use case. They filled in a structured questionnaire consisting of the measurements relating to the hypotheses. We evaluated the answers using partial least squares structural equation modeling (PLS-SEM). The tests conducted on the measurement model include checking internal consistency, convergent profitability, discriminant validity, and reliability of the indicators (Hair etal., 2011). The preliminary study contained 21 responses. (f) We created an extended UTAUT model for IPA adoption from the hypotheses validated in the preliminary survey (cf. Venkatesh, 2022). The extended UTAUT model and hypotheses were assessed in the main study (see Model Evaluation and Hypotheses Evaluation). To this end, we recruited native Englishspeaking employees with daily touch points with processes involving digital technologies from different organizations. Since IPA is a novel technology and may not be known to the participants in detail, we provided a comprehensive explanation of the concept before the survey and illustrated it with some real-life examples of observed and unobserved intelligent robots. To counteract the problem of careless responses and the associated suboptimal data quality, we used an attention check (Pei etal., 2020). Survey answers were again evaluated with PLS-SEM as it constitutes a solution for small sample sizes and complex models with many constructs and a large number of items (Hair etal., 2019; Willaby etal., 2015). It also causes low bias in reflective measurement models, which approach zero at sample sizes of n = 100 and above (Sarstedt etal., 2016). The assessment of the results follows the guidelines by Hairet al. (2014) and Hair etal. (2019). The related calculations were performed via Smart-PLS 3 (Ringle etal., 2015). We used bootstrapping with 500 resamples iterative model optimization (Kock & Hadaya, 2018). For the final derivation of the model parameters, we used bootstrapping with 5000 resamples (Hair etal., 2014). To further explore the results, we conducted an importance-performance map analysis (Hair etal., 2019). Importance-performance map analysis was developed to prioritize management actions for efficient resource allocation (Martilla & James, 1977). It enables the comparison of the total effects on a defined target construct. Comparison is made in the dimensions of performance and importance in relation to the target construct (Ringle & Sarstedt, 2016). Electronic Markets (2024) 34:5656 Page 6 of 22 Derivation ofdeterminants andhypotheses The structured literature review and concept matrix creation resulted in 13 potential determinants for IPA adoption. The concept matrix is shown in Appendix 1. In each contribution, at least one construct of the UTAUT basic model according to Venkatesh etal. (2003) was used, namely, Performance Expectancy (n = 71), Behavioral Intention (n = 69), Effort Expectancy (n = 64), Social Influence (n = 48), Facilitating Conditions (n = 36), or Use Behavior (n = 27). Furthermore, various extensions of the model with the constructs Trust (n = 32), Attitude Towards Using IPA (n = 25), Perceived Risk (n = 25), Pricing Value (n = 13), Hedonic Motivation (n = 11), Transparency (n = 8), and Anxiety (n = 5) were observed. Table1 shows the operational definitions of the constructs. Table2 shows role, occupational focus, and experience of the practitioners that were consulted for validation of the identified constructs. To this end, the practitioners rated the perceived relevance of each identified determinant on a 5-point Likert scale during the interviews. A rating of 1 indicates low relevance, a rating of 5 indicates high relevance. Table3 shows their ratings. All median values are above 2.0 (= rather not relevant for adoption). Accordingly, we considered all determinants as potentially relevant for further model development. The hypotheses were derived analogously to UTAUT and related literature. As consequence of focusing on UTAUT, we included Behavioral Intention and Use Behavior as dependent constructs as they directly relate to technology adoption. Behavioral Intention relates to an employee’s subjective willingness to consistently use IPA (Venkatesh etal., 2012). Going beyond the intention, Use Behavior refers to concrete actions to adopt IPA in operational practice (Venkatesh etal., 2012). Appendix 2 shows the relationship between the hypotheses and the literature references. In addition to the identified determinants, we consider moderators that are diffused in the UTAUT literature, which are age, experience, and gender (Venkatesh etal., 2012). Job level is considered a further moderator, as its relevance is explicitly clarified in the expert interviews. Table4 Table 1 Identified constructs Construct Short Operational Definition Anxiety AN Sum of rational and irrational feelings of fear or anxiety experience when interacting with IPA Attitude AT General affective response to the use of IPA Effort Expectancy EE Degree of perceived ease of use of IPA Facilitating Conditions FC Extent of belief that an organizational and technical infrastructure exists to support the use of IPA Hedonic Motivation HM Joy or pleasure that comes from using IPA Performance Expectancy PE Extent of belief that using IPA will help improve work performance Perceived Risk PR Sum of all perceived risks associated with the use of IPA Pricing Value PV Cognitive trade-off between the perceived benefits and the monetary costs of IPA Social Influence SI Extent of perception that others believe that the individual should use IPA Trust TT The degree of confidence in the specific technology IPA Transparency TY The extent of comprehension and understanding of the internal processes and the output of IPA Table 2 Characteristics of consulted practitioners # Role Focus Experience (yrs) E1 Senior researcher Hyperautomation, explainable AI 3 E2 Senior researcher Hyperautomation, explainable AI 4 E3 Partner Customer relationship management, cloud computing 4 E4 Head of digital process consulting BPM, RPA, IPA 10 Table 3 Ratings of construct relevance by consulted practitioners (1 = low relevance, 5 = high relevance) # AN AT EE FC HM PE PR PV SI TT TY E1 5 3 2 4 1 5 4 4 3 4 4 E2 5 4 2 4 3 5 4 4 2 5 2 E3 5 5 3 2 4 5 3 5 5 4 1 E4 4 4 5 4 3 5 3 5 5 5 3 Median 5 4 2.5 4 3 5 3.5 4.5 4 4.5 2.5 Electronic Markets (2024) 34:56 Page 7 of 22 56 summarizes the formulated hypotheses, which are outlined in the following. We assume a positive influence of Performance Expectancy on Behavioral Intention for two reasons (H1a). First, this influence exists in the UTAUT reference model and in IPA-related work, such as on RPA (Wewerka etal., 2020) and chatbots (Danckwerts etal., 2020; Eißer etal., 2020; Laumer etal., 2019; Meyer-Waarden etal., 2020). Second, the practical application of IPA demonstrates significant advantages in terms of efficiency and cost savings. Due to the ability to cost-effectively automate repetitive tasks, a connection to the intention of using IPA is assumed. The degree to which IPA is expected to be useful or powerful to leverage performance may positively relate to the Attitude towards using IPA (Dwivedi etal., 2019) (H1b). This applies in particular to the acceptance of software solutions by employees (Amin etal., 2016). Several identified contributions show the influence of Performance Expectancy on Attitude of technology in general as well as for AI-based tools (Liu etal., 2019; e.g., Pan etal., 2019). We adopt the assumption of moderating effects of Age and Gender on Performance Expectancy and Attitude in UTAUT (Venkatesh etal., 2003). Further, we consider Job Level as a moderator for the effects of Performance Expectancy (H1c, H1d). The interviewees explained that a critical point would exist where Performance Expectancy is so high that the respective technology would be perceived as a threat to employment, hindering the adoption. Analogous to Performance Expectancy, we assume a positive influence of Effort Expectancy on Behavioral Intention for two reasons (H2a). First, we orient towards the UTAUT reference model. Further, we identified contributions finding this influence specifically for IPA (Eißer etal., 2020; e.g., Wewerka etal., 2020). Second, since IPA often operates at the user level of software via robots, there is no need for costly and extensive modifications related to the software associated with the process to be automated (Syed etal., 2020). In line with the idea of automation and more efficient resource utilization, we also assume a positive influence of Effort Expectancy on Performance Expectancy in the context of IPA (H2b). The influence of Effort Expectancy on Performance Expectancy was observed in the identified contributions investigating IPA (Eißer etal., 2020; e.g., Wewerka etal., 2020). The degree to which IPA use is perceived as complex compared to other technologies could Table 4 Identified hypotheses _ → significant negative influence, + → significant positive influence, * moderating influence Age (AGE), Anxiety (AN), Attitude (AT), Behavioral Intention (BI), Effort Expectancy (EE), Experience (EXP), Facilitating Conditions (FC), Gender (GDR), Hedonic Motivation (HM), Job Level (JOL), Performance Expectancy (PE), Perceived Risk (PR), Price Value (PV), Social Influence (SI), Trust (TT), Transparency (TY), Use Behavior (UB) # Hypotheses # Hypotheses # Hypotheses 1a PE + → BI 4f FC * AGE, EXP, GDR → BI 7h TT * AGE, EXP, GDR → EE 1b PE + → AT 4g FC * AGE, EXP, GDR → EE 7i TT * AGE, EXP, GDR → PE 1c PE * AGE, GDR, JOL → BI 4h FC * AGE, EXP, GDR → PE 7j TT * AGE, EXP, GDR → PR 1d PE * AGE, GDR, JOL → AT 5a AT + → BI 8TY + → TT 2a EE + → BI 5b AT + → UB 9a AN _ → BI 2b EE + → PE 6a PR _ → BI 9b AN + → PE 2c EE + → AT 6b PR _ → PE 9c AN _ → EE 2d EE * AGE, GDR, EXP → BI 6c PR _ → AT 9d AN * AGE, EXP, GDR, JOL → BI 2e EE * EXP → AT 6d PR * AGE, GDR → BI 9e AN * AGE, EXP, GDR, JOL → PE 3a SI + → BI 6e PR * AGE, GDR → PE 9f AN * AGE, EXP, GDR, JOL → EE 3b SI + → AT 6f PR * AGE, GDR →AT 10a HM + → BI 3c SI * AGE, EXP, GDR, JOL → BI 7a TT + → BI 10b HM * AGE, EXP, GDR, JOL → BI 3d SI * AGE, EXP, GDR, JOL → AT 7b TT + → AT 11a PV + → BI 4a FC + → UB 7c TT + → EE 11b PV * AGE, EXP, GDR, JOL → BI 4b FC + → BI 7d TT + → PE 12a BI + → UB 4c FC + → EE 7e TT _ → PR 12b BI * EXP → UB 4d FC + → PE 7f TT * AGE, EXP, GDR → BI 4e FC * AGE, EXP → UB 7g TT * AGE, EXP, GDR → AT Electronic Markets (2024) 34:5656 Page 8 of 22 also positively influence the Attitude towards the technology (Dwivedi etal., 2019) (H2c). The influence between Effort Expectancy and Attitude has been observed in adoptionrelated literature concerning AI-based tools (Cao etal., 2021; e.g., Pan etal., 2019). We incorporate the assumption that Age, Gender, and Experience moderate the effect from Effort Expectancy on Behavioral Intention from the UTAUT reference model (Venkatesh etal., 2003) (H2d). Further, the interviewees mentioned that Effort Expectancy and its effects strongly depend on users’ experience influencing their Attitude (H2e). For instance, Effort Expectancy in the use of IPA tools, tends to be lower if the user has experience with comparable technologies. We posit a positive influence of Social Influence on Behavioral Intention (H3a). Consistent with the UTAUT reference model, identified contributions indicate this influence regarding AI-based tools (Aboelmaged, 2010; Cox, 2012; Gao etal., 2015; Handoko etal., 2018; Hsu etal., 2014; Lee & Song, 2013; Lee, 2009; Li etal., 2020; Slade etal., 2015; Wang etal., 2015). UTAUT meta-studies corroborate this impact (Dwivedi etal., 2019; e.g., Williams etal., 2015). Analogously, we assume an influence of Social Influence on Attitude (H3b). We justify the assumption by the potential influence of third parties who have adopted or rejected the respective technology on the attitudes of potential users (Dwivedi etal., 2019). The influence of Social Influence on Attitude has been observed in literature on RPA (e.g., Wewerka etal., 2020) and AI (e.g., Peters etal., 2020). We adopt Age, Gender, and Experience as moderators on the relations of Social Influence on Behavioral Intention and Attitude from the UTAUT reference model (Venkatesh etal., 2003). Additionally, the interviewees emphasized that Social Influence towards IPA adoption is exerted less frequently at the same hierarchical level but primarily between Job Levels (H3c, H3d). It can be inferred that the influence of Social Influence increases with the number of hierarchical levels above. Regarding Facilitating Conditions, we assume a positive influence on Use Behavior (H4a). Facilitating Conditions such as the management of high data volume and consistent data quality comprise major challenges in the implementation of AI-based automation (Jyoti & Szurley, 2021). Also, Facilitating Condition influences Use Behavior in the UTAUT reference model (Venkatesh etal., 2003). The influence has additionally been proven in a meta-analysis (Williams etal., 2015). In the UTAUT reference model, Facilitating Conditions do not directly influence Behavioral Intention. Venkatesh etal. (2003) argued that the explanatory power of Facilitating Conditions on Behavioral Intention could only be demonstrated if Performance Expectancy and Effort Expectancy are not included in the model. Dwivedi etal. (2019) point out that this limitation does not hold true in every configuration, which is supported by the results of our structured literature review. In the context of UTAUT2, Venkatesh etal. (2012) argued that individuals with access to an advantageous set of Facilitating Conditions exhibit a higher willingness to adopt a technology. Therefore, the positive influence of Facilitating Conditions on Behavioral Intention cannot be excluded in the context of IPA (H4b). Furthermore, we assume a positive influence of Facilitating Conditions on Effort Expectancy (H4c). This is justified by Facilitating Conditions being a direct determinant of Effort Expectancy in the acceptance of new software solutions by employees (Amin etal., 2016) and technology acceptance in general (Venkatesh & Bala, 2008). Moreover, Facilitating Conditions could exert a positive influence on Performance Expectancy (H4d). This is justified by the provision of appropriate training and a sufficiently high-quality technical and organizational infrastructure, which assist potential users in gaining clarity about the actual system performance (Chatterjee & Bhattacharjee, 2020). The implied effect between Facilitating Conditions and Performance Expectancy has been observed in AI-based tools (e.g., van Hung etal., 2021), especially in the business context (e.g., Cao etal., 2021). We adopt the moderating effects regarding Facilitating Conditions according to the UTAUT reference model (H4e-h). In the UTAUT reference model, there is no significant effect on Behavioral Intention or Use Behavior due to potential overlaps with Performance Expectancy and Effort Expectancy (Venkatesh etal., 2003). We do include positive influences as more recent research indicates that Attitude can be a relevant determinant in the adoption and usage of innovative technologies (Dwivedi etal., 2017) (H5a). Furthermore, it has been demonstrated that Attitude can be a direct determinant of Behavioral Intention in the acceptance of software by employees (Amin etal., 2016; Morris etal., 2005; Pan etal., 2019). The positive influence of Attitude on Use Behavior is examined separately (H5b). A general aversion towards algorithms inherent to AI-based tools may have a significant impact on IPA adoption (Berger etal., 2021). Usage behavior is thus influenced by Attitude (Venkatesh, 2022). We assume a negative influence of Perceived Risk on Behavioral Intention, Performance Expectancy, and Attitude in the context of IPA adoption. Potentially Perceived Risks are manifold, such as financial risks or Performance Risks in case IPA works less efficiently than assumed. The negative influence of Perceived Risk on Behavioral Intention has been observed in a large number of identified contributions on IPA (e.g., Huang & Wang, 2009; Laumer etal., 2019) and generally in AI-based tools (H6a) (Gao etal., 2015; Jianbin & Jiaojiao, 2013; M.-C. Lee, 2009; J. Li etal., 2019; Slade etal., 2015). In addition to the absolute benefit (Davis, 1989), Performance Expectancy also includes the relative advantage (Moore & Benbasat, 1991) Electronic Markets (2024) 34:56 Page 15 of 22 56 (Willcocks etal., 2015). Furthermore, a positive Attitude can be cultivated by stressing the importance of and counteracting prevalent Perceived Risk and Anxiety. Perceived Risk’s negative effect could be countered by the implementation of risk management (Power, 2004, 2009), including A/B testing (Deng etal., 2017), bandit services (Malekzadeh etal., 2020), and canary deployments (Tarvo etal., 2015). Robots could also have the ability to run without visual representation to ensure privacy (Syed etal., 2020). The negative influence of Anxiety should be remediated through continuous sensitization. In particular, Anxiety about losing one’s job due to automation should be addressed to foster IPA adoption. Establish facilitating conditions We find that organizations can influence IPA adoption establishing Facilitating Conditions. Specifically, our results show direct effects of Facilitating Conditions on Effort Expectancy and Performance Expectancy and indirect effects on Attitude. These effects suggest that organizations should provide appropriate tools and support employees in the use of IPA. The establishment of hands-on training to demonstrate IPA use and entailed advantages of automation constitutes an exemplary Facilitating Condition (Alshare & Lane, 2011; Sabherwal etal., 2006). Helpdesks can be established to ensure continuous support for both initial or ongoing IPA use (Coeurderoy etal., 2014). To influence Performance Expectancy, Effort Expectancy, and Attitude positively, infrastructures should facilitate IPA integration into daily operational practice. Additionally, designing user-friendly interfaces of IPA tools supports its adoption (Zuiderwijk etal., 2015). Mind experience ofusers Our results show that IPA adoption depends on user characteristics, in particular Experience. The positive effect of Experience on Use Behavior implies that potential adopters who have Experience are more likely to use IPA than workers without prior Experience. The positive direct effect of Experience on Effort Expectancy also implies that potential adopters who have Experience perceive the use of the technology to be easier than workers without related prior knowledge. Furthermore, the moderating effect of Experience between Price Value and Behavioral Intention suggests that Price Value is increasingly negatively perceived by individuals with Experience. Additionally, the observable positive moderating effects of Job Level between Pricing Value and Behavioral Intention and Pricing Value and Use Behavior imply that as Job Level increases, the Pricing Table 8 Summary of results and implications _ → significant negative influence, + → significant positive influence, * moderating influence Age (AGE), Anxiety (AN), Attitude (AT), Behavioral Intention (BI), Effort Expectancy (EE), Experience (EXP), Facilitating Conditions (FC), Gender (GDR), Hedonic Motivation (HM), Job-Level (JOL), Performance Expectancy (PE), Perceived Risk (PR), Price Value (PV), Social Influence (SI), Trust (TT), Transparency (TY), Use Behavior (UB) Implications # Hypotheses Cultivate a positive attitude towards IPA 1b PE + → AT 2c EE + → AT 5a AT + → BI 6c PR _ → AT 7g TT * AGE, EXP, GDR → AT 12a BI + → UB Establish facilitating conditions 4c FC + → EE 4d FC + → PE 2b EE + → PE Mind experience of users 11b PV * AGE, EXP, GDR, JOL → BI 7j TT * AGE, EXP, GDR → PR 9c AN _ → EE 10a HM + → BI Transparency is no end in itself 8TY + → TT 7d TT + → PE 7e TT _ → PR Electronic Markets (2024) 34:5656 Page 16 of 22 Value of the technology is increasingly perceived positively or weighted more highly. This entails that providing job-level adequate training on the capabilities of IPA and intelligent systems or machine learning in general could improve the overall adoption of such systems. Our finding is in line with research that finds that the level of work experience influences how information is perceived and information systems used. Future research can expand on this to disentangle relations among experience, job-levels, and required training to further understand determinants of IPA adoption with regard to user experience (Kalyuga etal., 2003; Mayer & Moreno, 2003). Transparency isnoend initself In their call for research, Engel etal. (2022) explicitly refer to a need for investigations on making decisions of IPA tools explainable to users to foster IPA use. In this vein, we can confirm the relevance of Transparency and Trust in the context of AI-based technologies as highlighted by Venkatesh (2022). Accordingly, we propose to integrate the constructs Trust and Transparency for acceptance research around IPA as well as related technologies as integral constructs in future research models. We agree with research on explainability of AI in that the creation of Transparency over AI-related technology can facilitate Trust and therefore adoption. This implication is consistent with the observations and assumptions of Kalimeri and Tjostheim (2020), Lipton (2018), and Wanner etal. (2022) that the explainability or transparency of models is a prerequisite for the formation of Trust. Lacity etal. (2016) were able to derive comparable findings when interviewing senior executives in an RPA context. Supporting the positive effects of Trust, we identified a strong negative effect on Perceived Risk. A direct moderating effect of Experience between Trust and Perceived Risk suggests that the effect may increase with Experience. Related research on explainability and adoption of AIrelated technology has revealed that Trust can be improved through various measures, including implementing and communicating frameworks for trustworthy AI and developing organizational trust management (Thiebes etal., 2021). In particular, Transparency can be facilitated by the provisioning of comprehensive global and local explanations of the inner workings as well as the representation of current process flows and by implementing feedback loops that reveal the states of software robots and including inputs and outputs (Holder etal., 2021). As our research shows, Transparency works through the Trust and Perceived Risk as well as Performance Expectancy relation. Hence, we posit it is insufficient to provide “explanations” that merely make things transparent by providing data and information but focus on user-centered explanations that provide clarity and understanding about decisions of IPA tools, such as for example predictions (Herm etal., 2023). In the context of IPA, this may be even more important than for decisional AI as the tasks of the AI involve not only decision-making but also task execution. Consequently, this work relation between human and IPA resembles a delegation situation rather than a software selection decision which makes bridging the information asymmetry between the two parties ever more important. Limitations Our research has some methodological and content-wise limitations to be considered when interpreting and using the results. In terms of methodology, the literature review and concept matrix creation have subjective components, such as the exclusion of publications. To mitigate this potential limitation, we strictly adhered to guidelines diffused in Information Systems (vom Brocke etal., 2009; i.e., vom Brocke etal., 2015; Webster & Watson, 2002). Moreover, there are limitations inherent to online studies as these do not enable to monitor participants directly. Prolific.com includes a live chat to answer immediate questions but cannot compensate for a lack of personal interaction. To ensure the quality of answers, we screened results for irregular execution times and also on the basis of attention checks. Moreover, established, generic models like UTAUT provide only one structured approach to studying acceptance (Williams etal., 2009). We chose to use such a model because we are among the first to investigate IPA adoption on this scale. By doing so, we build on a large body of existing knowledge, comparable to many recent innovative contributions in Information Systems (e.g., Hooda etal., 2022; Misra etal., 2022; Wanner etal., 2022; Xu etal., 2024). At the same time, we create opportunities for future research to explore other approaches, such as purely qualitative, exploratory studies that are less restricted to long-established constructs. In this vein, we find novel approaches of assessing technology acceptance that are diffusing in Information Systems research. For example, Baird and Maruping (2021) emphasize the need of considering agency regarding the adoption of AI artifacts. Further, several researchers propose integrating theoretical notions on task-technology fit with TAM and UTAUT to explain variance in user adoption (e.g., Bouwman & van de Wijngaert, 2009; van Huy etal., 2024). While upcoming approaches like these seem promising also for the context of IPA, their relatively low level of diffusion reduces comparability. In terms of content-wise limitations, we find that the openness or restrictiveness of an organization may influence the user’s attitude towards adoption and serve as an interested playing field to analyze related aspects such as workarounds to use AI and quiet quitting. Further, research has also shown that Electronic Markets (2024) 34:56 Page 17 of 22 56 constructs such as Social Influence and Perceived Risk can be dependent on the cultural background of the respondents (e.g., Bandyopadhyay & Fraccastoro, 2007; Verhage etal., 1990). The empirical survey was conducted in English only. Due to the inseparable link between language and culture, people from different cultural backgrounds may have been excluded (Jiang, 2000). This imperfection of the research (Williams etal., 2015) could lead to a bias in the results, leaving potential for future investigations. Conclusion andfuture work IPA leverages advantages of symbolic process automation with AI to automate complex business processes requiring decision-making capacities. Despite the economic pressure to take advantage of IPA and its potential competitive advantages, the adoption rate of IPA is comparatively low. To understand reasons and identify areas for action towards IPA adoption as considered explicitly necessary in IS research (Engel etal., 2022), we identified 13 determinants and created an extended UTAUT model (cf. Table1). Providing normative knowledge with the UTAUT extension, we show influential relations between identified determinants for IPA adoption. In particular, we find that it is important to cultivate a positive attitude towards IPA, establish suitable facilitating conditions, especially mind user experience, and embrace the fact that transparency is no end in itself and does need to provide explainability of system behavior rather than “explanations” in terms of mere data and information. Our research entails two starting points for future research. First, further studies with a larger number of participants or with a focus on certain participant characteristics, such as culture, can further test robustness and contingencies of our developed model. Second, research can further extend the model with more determinants originating from practical applications, for example by conducting research based on case studies. As a bridge towards other design-oriented research, this research can be used to inform requirements engineering and the design of complex IPA building blocks such as data trust models where complex and flexible interactions with multiple parties exceed the boundaries of symbolic process automation. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s1252502400737-9. Acknowledgements This research and development project is funded by the German Federal Ministry of Education and Research(BMBF) within the “Richtlinie zur Förderung von Projekten zur Erforschung oder Entwicklung praxisrelevanterLösungsaspekte (“Bausteine”) für Datentreuhandmodelle” (Funding No. 16DTM201B) and financed by theEuropean Union - NextGenerationEU. The authors are responsible for the contents of this publication. Funding Open Access funding enabled and organized by Projekt DEAL. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. References Aboelmaged, M. G. (2010). 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