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The use of innovative technologies by firm employees is a key factor in ensuring the competitiveness of firms. However, researchers and practitioners have been concerned about the willingness of technology end users to use innovative technologies. This study, therefore, aims to determine the factors affecting the intention to use marketing analytics technology.

This study surveyed 213 firm employees. The quantitative data collected was analysed using partial least squares structural equation modelling.

The results reveal that performance expectancy, facilitating conditions, attitudes and perceived trust have a positive and significant effect on intentions to use marketing analytics. Effort expectancy, social influence and personal innovativeness in information technology were found not to predict intentions to use marketing analytics.

This study has practical implications for firms seeking to enhance the use of marketing analytics technology in developing countries.

This study contributes to the use of UTAUT, perceived trust, personal innovativeness and user attitude in predicting the intentions to use marketing analytics technology.

This paper aims at identifying the key factors influencing the adoption intention of data analytics for predictive maintenance (PdM) from the lens of the Technology–Organization–Environment (TOE) framework in the Singapore Process Industries context. The research model aids practitioners and researchers in developing a holistic maintenance strategy for large-scale asset-heavy process industries.

The TOE framework has been used in this study to consider a wide set of TOE factors and develop a research model with the support of literature. A survey is undertaken and the structural equation modelling (SEM) technique is adopted to test the hypotheses of the proposed model.

This research highlights the significant roles of digital infrastructure readiness, security and privacy, top management support, organizational competence, partnership with external consultants and government support in influencing adoption intention of data analytics for PdM. Perceived challenges related to organizational restructuring and process automation are not found significant in influencing the adoption intention.

This paper reports valuable insights on adoption intention of data analytics for PdM with relevant implications for the various stakeholders such as the leaders and senior managers of process manufacturing industry companies, government agencies, technology consultants and service providers.

This research uniquely validates the model for the adoption of data analytics for PdM in the process industries using the TOE framework. It reveals the significant technology, organizational and environmental factors influencing the adoption intention and highlights the relevant insights and implications for stakeholders.

The purpose of this article is to identify how the organisations are able to improve their business value through acquisition of business analytics capabilities and by improving their performance.

With the help of literature survey, along with standard resource-based view framework, a conceptual model has been developed. These have been statistically tested by collecting the data using the survey questionnaire from 306 selected respondents from various service sector- and product-based organisations in India. To analyse the data we have used partial least square–based structural equation modelling.

The study highlights that by the help of data acquisition and tool acquisition as two vital components the acquisition of business analytics capabilities could improve the business value of the organisation by strengthening its organisational performance. The findings of this research also indicated that acquisition of business analytics capabilities has a significant influence on an organisation's business process performance and business decision, which in turn significantly influences organisational performance. And organisational performance eventually positively influences its business value. The model was found to provide an explanative power of 71%.

The proposed research model can provide effective recommendations to the management of the organisations to realise the importance of acquisition of effective business analytics capabilities to eventually improve the business value of the organisation.

No specific studies, as yet, have analysed the effects of acquisition of business analytics capabilities for improving organisational performance mediated through business process performance and business decision. Therefore, this research has explored the distinctive effort to empirically validate this understanding.

Based on resource-based and dynamic capabilities theorizing, this study explores how human resource analytics (HRA) can improve human resource management (HRM) performance and organizational performance, with creative problem-solving capability (CPSC) as an underlying mediator for creating value from HRA. It also explores how data quality and HRA personnel expertise act as moderators in this relationship.

Hypotheses are tested in an empirical study including 191 firms using partial least square structural equation modeling technique.

The findings confirm the direct and indirect effect of HRA use and maturity on HRM and organizational performance, as well as the mediating role of CPSC. HRA personnel expertise was found to moderate the relationship between HRA and CPSC, data quality being an important factor.

The findings contribute to the sparse evidence of value creation from HRA use/maturity on HRM and organizational outcomes, providing a theoretical logic of resource-based view and dynamic capabilities view based on the underlying causal mechanism through which HRA creates value. The study identified complementary capabilities which when combined with HRA use/maturity and CPSC result in value creation.

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.

FinTech offers numerous prospects for significant enhancements and fundamental changes in financial services. However, along with the myriad of benefits, it also has the potential to induce risks to individuals, organisations and society. This study focuses on understanding FinTech developers’ perspective of the dark side of FinTech.

This study conducted semi-structured interviews with 23 Nigerian FinTech developers using an exploratory, inductive methodology The data were transcribed and then thematically analysed using NVivo.

Three themes – customer vulnerability, technical inability and regulatory irresponsibility – arose from the thematic analysis. The poor existing technological infrastructure, data management challenges, limited access to data and smartphone adoption pose challenges to a speedy integration of FinTech in the country, making customers vulnerable. The lack of privacy control leads to ethical issues. The lack of skilled developers and the brain drain of good developers present additional obstacles to the development of FinTech in Nigeria.

FinTech operation in a developing country differs from that in developed countries with better technological infrastructure and institutional acceptance. This study recognises that basic banking operations through FinTech are still not well adopted, necessitating the need to be more open-minded about the global practicalities of FinTech.

FinTech managers, banks and policymakers can ethically collect consumer data that can help influence customer credit decisions, product development and recommendations using the mobile app and transaction history. There should be strict penalties on FinTech for selling customers’ data, sending unsolicited messages or gaining unnecessary access to the customer’s contact list. FinTech can offer to educate consumers about their financial management skills.

Whereas other studies have focused on the positive aspects of FinTech to understand client perceptions, this study offers new insights into the dark side of FinTech by analysing the viewpoints of FinTech developers. Furthermore, the study is based in Nigeria, an emerging economy adopting FinTech, adding a new dimension to the body of knowledge.

Understanding the adoption of m-commerce in small and medium-sized enterprises (SMEs) is critical for their sustainable development. This study aims to investigate the adoption of m-commerce in Vietnamese SMEs, leading to the identification of the critical determinants and their relative importance for m-commerce adoption.

An integrated model is developed by combining the diffusion of innovation theory and the technology–organization–environment framework. Such a model is then tested and validated using structural equation modeling and artificial neural networks in analyzing the survey data.

The study indicates that perceived security is the most critical determinant for m-commerce adoption. It further shows that customer pressure, perceived compatibility, organizational innovativeness, perceived benefits, managers’ IT knowledge, government support and organizational readiness all play a critical role in the adoption of m-commerce in Vietnamese SMEs.

The findings of this study can lead to the formulation of better strategies and policies for promoting the adoption of m-commerce in Vietnamese SMEs. Such findings are also of practical significance for the diffusion of m-commerce in SMEs in other developing countries.

To the best of the authors’ knowledge, this is the first attempt to explore the adoption of m-commerce in Vietnamese SMEs using a hybrid approach. The application of this approach can lead to better understanding of the relative importance of the critical determinants for the adoption of m-commerce in Vietnamese SMEs.

Online employee reviews have emerged as a crucial information source for business managers to evaluate employee behavior and firm performance. The purpose of this paper is to test the relationship between employee personality traits, derived from online employee reviews and job satisfaction and turnover behavior at the individual level.

The authors apply text-mining techniques to extract personality traits from online employee reviews on Indeed.com based on the Big Five theory. They also apply a machine learning classification algorithm to demonstrate that incorporating personality traits can significantly enhance employee turnover prediction accuracy.

Personality traits such as agreeableness, conscientiousness and openness are positively associated with job satisfaction, while extraversion and neuroticism are negatively related to job satisfaction. Moreover, the impact of personality traits on overall job satisfaction is stronger for former employees than for current employees. Personality traits are significantly linked to employee turnover behavior, with a one-unit increase in the neuroticism score raising the probability of an employee becoming a former employee by 0.6%.

These findings have implications for firm managers looking to gain insights into employee online review behavior and improve firm performance. Online employee review websites are recommended to include the identified personality traits.

This study identifies employee personality traits from automated analysis of employee-generated data and verifies their relationship with employee satisfaction and employee turnover, providing new insights into the development of human resources in the era of big data.

The primary objective of this study is to explore consumers' non-adoption intentions towards meta-commerce (or metaverse retailing). Utilizing the Innovation Resistance Theory (IRT) as the theoretical foundation, this study investigates the impact of diverse barriers on non-adoption intentions within the meta-commerce context.

A total of 356 responses were gathered to test the proposed hypotheses. Structural Equation Modelling (SEM) with SmartPLS 4 software was used to examine these hypotheses.

The findings of this study show that perceived cyber risk, perceived regulatory uncertainty, perceived switching cost and perceived technical uncertainty are significantly linked to non-adoption intention towards meta-commerce. Furthermore, the study suggests that the moderating influence of technostress on these connections is more pronounced for consumers with high technostress compared to those with low technostress.

This study makes a significant contribution to the current body of literature by providing valuable insights into the fundamental barriers that consumers encounter when contemplating the adoption of meta-commerce. This contribution is particularly noteworthy as it fills a gap in the existing literature, as no prior study has comprehensively examined the primary obstacles that shape consumer intentions towards meta-commerce adoption. This novel perspective offers scholars, businesses and policymakers a foundation for developing strategies to address these barriers effectively.

This paper aims to present a review of small and medium-sized enterprise (SME) literature on the adoption of technology for marketing with the objective the knowledge trajectory from SMEs to tourism SMEs (TSMEs) and presents a research agenda for the adoption of digital marketing among TSMEs.

The quantitative literature review method is adopted to analyze 50 articles published in high-ranking journals from 2011–2021. The selected papers were reviewed to quantitatively present the popular theoretical frameworks, constructs and research methods used for the adoption of digital marketing among SMEs and to present a future research agenda in the context of TSMEs.

The findings present a review of theoretical approaches, research design, methods, and models used by researchers, and identify new directions for future research in the context of the adoption of digital marketing among TSMEs. The paper presents a theoretical critique of the technology adoption theories and builds on diffusion of innovation theory, technology–organization–environment (TOE), and institutional theory to propose an expanded model to study digital marketing adoption among TSMEs.

The article presents a significant advancement in the theoretical understanding of the adoption of digital marketing by offering the application of the TOE framework with moderating role of mimetic and normative pressures. Thus, the study contributes to the scarce research on institutional theory in the context of services (tourism) and offers an alternative and holistic perception to study digital marketing. The research model serves as an organizing tool for future researchers and practitioners to probe and further augment knowledge development in tourism and TSMEs.

Negligible published work on TSMEs makes this study valuable and the proposed model gives strategic information in the given context.