Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption

Beduè, Patrick; Fritzsche, Albrecht

doi:10.1108/jeim-06-2020-0233

Cited by 85 publications

(53 citation statements)

References 51 publications

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“…For example, UTAUT is used in analyzing the adoption of AI tools (Venkatesh, 2021), the technology acceptance model (TAM) is used in AI adoption in the hospitality industry (Pillai & Sivathanu, 2020), and TAM–technology–organizations–environment is used in AI adoption in manufacturing firms (Chatterjee et al, 2021). The role of trust in AI adoption is studied using the valence framework (Bedué & Fritzsche, 2021), and AHP is used in analyzing barriers to AI adoption in healthcare (Al Badi et al, 2021). Reputed theories like UTAUT and TAM predict adoption behavior.…”

Section: Discussionmentioning

confidence: 99%

Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective

Kar¹,

Kar

Gupta

2021

Intell Sys Acc Fin Mgmt

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Summary Artificial intelligence (AI) in business processes and academic research in AI has significantly increased. However, the adoption of AI in organizational strategy is yet to be explored in extant literature. This study proposes two conceptual frameworks showing hierarchical relationships among the various drivers and barriers to AI adoption in organizational strategy. In a two‐step approach, the literature study is first done to identify eight drivers of and nine barriers to AI adoption and validated by academic and industry experts. In the second step, MICMAC (matrice d'impacts croises‐multiplication appliqúe a un classment or cross‐impact matrix multiplication applied to classification) analysis categorizes the drivers and barriers to AI adoption in organizational strategy. Total interpretive structural modeling (TISM) is developed to understand the complex and hierarchical associations among the drivers and barriers. This is the first attempt to model the drivers and barriers using a methodology like TISM, which provides a comprehensive conceptual framework with hierarchical relationships and relative importance of the drivers and barriers to AI adoption. AI solutions' decision‐making ability and accuracy are the most influential drivers that influence other driving factors. Lack of an AI adoption strategy, lack of AI talent, and lack of leadership commitment are the most significant barriers that affect other barriers. Recommendations for senior leadership are discussed to focus on the leading drivers and barriers. Also, the limitations and future research scope are addressed.

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Section: Discussionmentioning

confidence: 99%

Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective

Kar¹,

Kar

Gupta

2021

Intell Sys Acc Fin Mgmt

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“…(2020, p. 286) also pointed out that trust is the most significant predictor of m-commerce adoption, as it strongly determines its success. In addition, Bedué and Fritzsche (2021) showed that an important path leading to better AI-based adoption is trust-building, which is why we chose to trust as a main psychological factor (organism) in the model.…”

Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

Exploring users' adoption intentions in the evolution of artificial intelligence mobile banking applications: the intelligent and anthropomorphic perspectives

Lee

Chen

2022

IJBM

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PurposeThe development of mobile technology has changed the traditional financial industry and banking sector. While traditional banks have adopted artificial intelligence (AI) techniques to deepen the development of mobile banking applications (apps), the current literature lacks research on the use of AI-based constructs to explore users' mobile banking app adoption intentions. To fill this gap, based on stimulus-organism-response (SOR) theory, two AI feature constructs as stimuli are considered, namely, perceived intelligence and anthropomorphism. This study then develops a research model to investigate how intelligence and anthropomorphism affect task-technology fit (TTF), perceived cost, perceived risk and trust (organism), which in turn influence users' AI mobile banking app adoption (response).Design/methodology/approachThis study used a convenience nonprobability sampling approach; a total of 451 responses were collected to examine the model. The partial least squares technique was utilized for data analysis.FindingsThe results show that intelligence and anthropomorphism increase users' willingness to adopt mobile banking apps through TTF and trust. However, higher levels of anthropomorphism enhance users' perceived cost. In addition, both intelligence and anthropomorphism have insignificant effects on perceived risk. The results provide theoretical contributions for AI-based mobile banking app adoption and offer practical guidance for bank planning to use AI to retain users.Originality/valueBased on SOR theory, this study reveals that as features, AI-enabled intelligence and anthropomorphism help us further understand users' perceptions regarding cost, risk, TTF and trust in the context of AI-enabled app adoption intentions.

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“…While there is great appreciation for the potential of AI to revolutionize organizations, there is still some uncertainty about its consequences on people and organizations ( Bedué and Fritzsche, 2021 ). Frick (2015) said, “as these machines evolve from tools to teammates, one thing is clear: Accepting them will be more than simply adopting new technology.” In collaborative initiatives, the acceptance or avoidance of AI is dependent on the perception of AI.…”

Section: Proposed Conceptual Model and Hypothesis Developmentmentioning

confidence: 99%

Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model

2022

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Social development organizations increasingly employ artificial intelligence (AI)-enabled tools to help team members collaborate effectively and efficiently. These tools are used in various team management tasks and activities. Based on the unified theory of acceptance and use of technology (UTAUT), this study explores various factors influencing employees’ use of AI-enabled tools. The study extends the model in two ways: a) by evaluating the impact of these tools on the employees’ collaboration and b) by exploring the moderating role of AI aversion. Data were collected through an online survey of employees working with AI-enabled tools. The analysis of the research model was conducted using partial least squares (PLS), with a two-step model – measurement and structural models of assessment. The results revealed that the antecedent variables, such as effort expectancy, performance expectancy, social influence, and facilitating conditions, are positively associated with using AI-enabled tools, which have a positive relationship with collaboration. It also concluded a significant effect of AI aversion in the relationship between performance expectancy and use of technology. These findings imply that organizations should focus on building an environment to adopt AI-enabled tools while also addressing employees’ concerns about AI.

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Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption

Cited by 85 publications

References 51 publications

Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective

Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective

Exploring users' adoption intentions in the evolution of artificial intelligence mobile banking applications: the intelligent and anthropomorphic perspectives

Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model

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