Search results

This study aims to investigate the factors influencing the adoption intention of artificial intelligence (AI) by micro, small and medium enterprises (MSMEs) in Jordan.

The study adopts the technology–organization–environment (TOE) model. It examines the moderating effects of innovation culture, employee digital skill level and market competition on the relationships between the independent and dependent variables. A survey was utilized to collect data from 537 MSME owners or managers in Jordan and employed partial least squares structural equation modeling to test the hypotheses.

The results of the study support seven out of eight hypotheses. Business innovativeness, management support, perceived benefits and technological infrastructure have positive and significant effects on AI adoption intention, while perceived costs have no significant effect. However, the innovation culture, employee digital skill level and market competition were found to moderate the relationships between some of the independent variables and dependent variables.

The study provides valuable insights and recommendations for MSME owners, managers, employees, policymakers, educators and researchers interested in promoting and facilitating AI adoption by MSMEs in Jordan.

The current attempt extends the TOE framework by adding significant constructs representing the three contexts. Moreover, it is one of the few studies that analyzed the factors influencing the adoption intention of AI by MSMEs in Jordan, which are significant to the Jordanian economy and represent 99.5% of enterprises.

The purpose of our study was two-fold: (1) to examine the effect of perceived value derived from perceived benefits and sacrifices in the adoption of artificial intelligence (AI) in talent acquisition and (2) to investigate the moderating role of human resource (HR) readiness in the association between perceived value and AI adoption intention.

A structured questionnaire was administered to 198 talent acquisition executives and HR professionals of Indian IT companies based on a purposive sampling technique. Partial least squares structural equation modeling (PLS-SEM) was used on the Smart PLS 2.0 platform to analyse the data and test the model.

Results revealed that perceived benefits and sacrifices significantly predict perceived value which significantly affects the HR professional’s AI adoption intention. The study further found that HR readiness moderates the link between perceived value and the intention of HR professionals to adopt AI in the talent acquisition process in the Indian IT industry.

IT companies are advised to continuously monitor and evaluate the performance of AI tools to ensure that they are meeting the recruitment process needs to leverage AI’s benefits in talent acquisition. This study seeks to provide the impetus for a planned AI adoption in talent acquisition.

This research provides ample evidence for the existing technology adoption theories. It explored the predictors of adoption by validating the value-based adoption model in the Indian context. It provides valuable insights into the practice of acquiring talents in the IT sector using artificial intelligence.

Drawing on the technology-organization-environment (TOE) framework, this study aims to investigate determinants of performance of artificial intelligence (AI) adoption in hospitality industry during COVID-19 and identifies the relative importance of each determinant.

A two-stage approach that integrates partial least squares structural equation modeling (PLS-SEM) with artificial neural network (ANN) is used to analyze survey data from 290 managers in the hospitality industry.

The empirical results reveal that perceived AI risk, management support, innovativeness, competitive pressure and regulatory support significantly influence the performance of AI adoption. Additionally, the ANN results show that competitive pressure and management support are two of the strongest determinants.

This research offers guidelines for hospitality managers to enhance the performance of AI adoption and presents policy-making insights to promote and support organizations to benefit from the adoption of AI technology.

This study conceptualizes the performance of AI adoption from both process and firm levels and examines its determinants based on the TOE framework. By adopting an innovative approach combining PLS-SEM and ANN, the authors not only identify the essential performance determinants of AI adoption but also determine their relative importance.

The introduction of artificial intelligence (AI) technology has had a substantial influence on the retail industry. However, AI adoption entails considerable responsibilities and risks for senior managers. In this study, the authors developed an evaluation and selection mechanism for successful AI technology adoption in the retail industry. The multifaceted measurement and identification of critical factors (CFs) can enable retailers to adopt AI technology effectively and maintain a sustainable competitive advantage.

The evaluation and adoption of organisational AI technology involve multifaceted decision-making for management. Therefore, the authors used the analytic network process to develop an AI evaluation framework for calculating the weight and importance of each consideration. An expert questionnaire survey was distributed to senior retail managers and 17 valid responses were obtained. Finally, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was used to identify CFs for AI adoption.

The results revealed five CFs for AI adoption in the retail industry. The findings indicated that after AI adoption, top retail management is most concerned with factors pertaining to business performance and minor concerned about the internal system's functional efficiency. Retailers pay more attention to technology and organisation context, which are matters under the retailers' control, than to external uncontrollable environmental factors.

The authors developed an evaluation framework and identified CFs for AI technology adoption in the retail industry. In terms of practical application, the results of this study can help AI service providers understand the CFs of retailers when adopting AI. Moreover, retailers can use the proposed multifaceted evaluation framework to guide their adoption of AI technology.

The purpose of this study is to develop a synthesized conceptual framework for artificial intelligence (AI) adoption in the field of business-to-business (B2B) marketing.

A conceptual development approach has been adopted, based on a content analysis of 59 papers in peer-reviewed academic journals, to identify drivers, barriers, practices and consequences of AI adoption in B2B marketing. Based on these analyses and findings, a conceptual model is developed.

This paper identifies the following two key drivers of AI adoption: the shortcomings of current marketing activities and the external pressure imposed by informatization. Seven outcomes are identified, namely, efficiency improvements, accuracy improvements, better decision-making, customer relationship improvements, sales increases, cost reductions and risk reductions. Based on information processing theory and organizational learning theory (OLT), an integrated conceptual framework is developed to explain the relationship between each construct of AI adoption in B2B marketing.

This study is the first conceptual paper that synthesizes drivers, barriers and outcomes of AI adoption in B2B marketing. The conceptual model derived from the combination of information processing theory and OLT provides a comprehensive framework for future work and opens avenues of research on this topic. This paper contributes to both AI literature and B2B literature.

The 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).

This 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.

The 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.

Based 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.

This study aims to investigate the adoption intention of artificial intelligence (AI) in family businesses through the perspectives of digital entrepreneurship and entrepreneurship orientation.

The study examines contributing factors explaining the adoption intention of AI in the context of family businesses. The developed research model is examined and validated using structural equation modelling based on 631 respondents' data. Purposeful sampling is used to collect the respondents' data.

The proposed model included two endogenous (i.e. business innovativeness and adoption intention) and six exogenous variables (i.e. affordances, culture and flexible design, entrepreneurial orientation, generativity, openness and technology orientation) through ten direct paths and three indirect paths. The results depicted the significant influence of all the exogenous variables on the endogenous variable reflecting support of all the hypotheses. The business innovativeness partially mediates the relationships of culture and flexible design, entrepreneurial orientation and technology orientation with adoption intention. Further, the results demonstrated a model variance of 24.6% for business innovativeness and 64.2% for adoption intention of artificial intelligence in the family business.

The study contributes to theoretical developments in entrepreneurship and family business research and AI's theoretical progress, especially to digital entrepreneurship.

Theoretically, it contributes to the literature of entrepreneurship, particularly digital entrepreneurship. Additionally, the research model adds to the role of entrepreneurial orientation and digital entrepreneurship in the emerging family entrepreneurship literature. Considering the scarcity of research in this field, the empirically validated model explaining critical antecedents of AI adoption intention in the family business is a foundation for discussion, critique and future research.

In the context of new workplace environment, this study aims to study and generate insights about artificial intelligence (AI) adoption in hiring process of firms. It is very relevant when AI is dramatically reshaping hiring function in the changing scenario.

The objectives are achieved with the help of three studies involving Delphi method to explore the criteria for AI adoption decision. Followed by two multi criteria decision-making techniques, i.e. analytic hierarchy process to identify weights of the criteria and fuzzy technique for order preference by similarity to ideal solution to assess the extent of AI adoption in hiring.

The findings reveal that information security and return on investment are considered two very important criteria by human resources managers while contemplating the adoption of AI in hiring process. It was found that AI adoption will be suitable at the sourcing and initial screening stages of hiring. And the suitability of the hiring stage where AI can be applied has been found to have changed from before and after the onset of COVID-19 pandemic situation. The findings and its discussion assist and enhance better decisions about AI adoption in hiring processes of firms amid changing scenario – external and internal to a firm.

Findings also highlight research implications for future research studies in this emerging area.

Results act as a starting point for other human resources managers, who are still pondering over the idea of adopting AI in hiring in future.

This paper through a systematic approach contributes by identifying important evaluation criteria influencing AI adoption in firms and extent of its application in the stages of hiring. It makes a substantial contribution to the under-developed yet emerging paradigm of AI based hiring in practice and research.

The purpose of this study is to explore and examine the determinants of artificial intelligence (AI) adoption by human resource management (HRM). Further, the impact of AI adoption by HR department on their effectiveness has also been tested.

A model explaining the antecedents of AI adoption by HRM is proposed in this study. The proposed model is based on task–organization–environment and task–technology fit models. A two-step partial least square-based structural equational modelling (PLS-SEM) has been used for testing the model. Data was collected from 210 HRM employees (only senior level or specialized HR positions), working in IT firms located in Delhi-NCR region.

Literature review shows that among others, organizational preparedness, perceived benefits and technology readiness determine AI adoption which in turn can make HR system more effective. Results of PLS-SEM support all hypothesized relationships and validate the proposed model.

Considering paucity of research on antecedents of AI adoption by human resource department, this study adds significantly to the body of knowledge. Additionally, based on the findings of statistical analysis, certain AI-related recommendations are given to HRM.

The use of artificial intelligence (AI) in the workplace is on the rise. To help advance research in this area, the authors synthesise the academic research and develop research propositions on the antecedents and consequences of AI adoption and application in the workplace to guide future research. The authors also present AI research in the socio-technical system context to provide a springboard for new research to fill the knowledge gap of the adoption and application of AI in the workplace.

This paper summarises the existing literature and builds a theoretically grounded conceptual framework on the socio-technical system theory that captures the essence of the impact of AI in the workplace.

The antecedents of AI adoption and application include personnel subsystem, technical subsystem, organisational structure subsystem and environmental factors. The consequences of AI adoption and application include individual, organisational and employment-related outcomes.

A research agenda is provided to identify and discuss future research that comprises not only insightful theoretical contributions but also practical implications. A greater understanding of AI adoption from socio-technical system perspective will enable managers and practitioners to develop effective AI adoption strategies, enhance employees' work experience and achieve competitive advantage for organisations.

Drawing on the socio-technical system theory, the proposed conceptual framework provides a comprehensive understanding of the antecedents and consequences of AI adoption and application in the work environment. The authors discuss the main contributions to theory and practice, along with potential future research directions of AI in the workplace related to three key themes at the individual, organisational and employment level.