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The importance of key account management (KAM) as a management technique in business-to-business markets has grown in recent years. The success of KAM programmes is highly dependent on the efforts of individual employees, specifically key account managers. Research on KAM at an individual level is important but lacking in the academic domain. This study aims to fill this gap by developing and evaluating a model of key account manager personality traits and how they impact the adoption of artificial intelligence (AI) technologies. The study also depicts the effect of the adoption of AI technologies on competitive advantage and firm performance.

The study examines how the adoption of AI technologies impacts firms’ competitive advantage and performance. The study used competitive advantage as a mediator and organisational culture as a moderator. A mixed-method analysis was used to conduct the study. In the first phase, an exploratory study was conducted using interviews with 26 key account managers from the automobile industry and thematic analysis to establish 9 constructs. In the second phase, which is a confirmatory study, 496 respondents finally responded to the questionnaire.

All constructs are used for confirmatory analysis and validate the data. Our research shows that key account managers’ adoption of AI technologies is influenced significantly by personality traits. Extraversion, agreeableness, conscientiousness, neuroticism and openness have substantial links to adopting AI technologies, which impacts firms’ competitive advantage and performance. Organisational culture significantly moderates the association between agreeableness and the adoption of AI technologies.

The findings of this research allow organisations to optimise team composition, customise training programs based on individual traits and incorporate personality assessments into recruitment processes for streamlined technology adoption and improved competitiveness. Overall, these actions aim to enhance AI integration, driving competitive advantage and client satisfaction.

This study stands out as one of the limited inquiries examining how the Big-five personality traits of key account managers influence the integration of AI technologies and its resulting impact on company performance. Therefore, this research makes notable contributions to the realms of organisational psychology and technology adoption studies.

This study aims to apply the theory of consumption value to analyze the utilization of Metaverse technologies within hospitality and tourism while examining the factors that impact consumer intentions to use the Metaverse.

This paper aims to consider an extensive study spanning the period October 2021 to March 2023 was conducted to understand the shifts in an individual's intention to use Metaverse technologies in hospitality.

The findings of this study confirm that individual attitudes to the Metaverse and trust in Metaverse technologies significantly impact their intention to use the Metaverse.

The study aims to provide fresh insights into how individuals perceive Metaverse technologies in the context of choosing hotels and resorts, which enriches the understanding of consumer behaviors around Metaverse technology in hospitality.

This study aims to consider not only tourist intentions to use the Metaverse but also how diverse consumption values impact user attitudes, an area currently underresearched.

With big data (BD), traditional supply chain is shifting to digital supply chain. This study aims to address the issues and challenges in the way toward the implementation of big data analytics (BDA) in sustainable supply chain management (SSCM).

The factors that affect the implementation of BDA in SSCM are identified through a widespread literature review. The PESTEL framework is used for this purpose as it covers all the political, economic, social, technological, environmental and legal factors. These factors are then finalized by means of experts' opinion and analyzed using structural equation modeling (SEM).

A total of 10 factors are finalized with 31 sub-factors, of which sustainable performance, competitive advantage, stakeholders' involvement and capabilities, lean and green practices and improvement in environmental performance are found to be the critical factors for the implementation of BDA in SSCM.

This research has taken up the case of Indian manufacturing industry. It can be diversified to other geographical areas and industry sectors. Further, the quantitative analysis may be undertaken with structured or semi-structured interviews for validation of the proposed model.

This research provides an insight to managers regarding the implementation of BDA in SSCM by identifying and examining the influencing factors. The results may be useful for managers for the implementation of BDA and budget allocation for BDA project.

The result includes green practices and environmental performance as critical factors for the implementation of BDA in SSCM. Thus the research establishes a positive relationship between BDA and sustainable manufacturing that ultimately benefits the environment and society.

This research addresses the challenges in the implementation of BDA in SSCM in Indian manufacturing sector, where such application is at its nascent stage. The use of PESTEL framework for identifying and categorizing the factors makes the study more worthwhile, as it covers full spectrum of the various factors that affect the strategic business decisions.