Search results

The importance of workplace inclusion continues to gain scholarly acclaim. However, in reality, many employees choose to ostracize their colleagues post workplace relationship strife. With this notion the present study aims to delve into the intricate linkages between workplace relationship conflict (WRC) and employee ostracism behavior (EOB), exploring the serial mediating roles of relational identification (RI) and emotional energy (EE). Additionally, the study examines the potential moderating effect of perceived forgiveness climate (PFC) to understand how forgiveness climate may serve as a boundary condition in shaping these crucial relationships.

The analysis utilized five-wave time-tagged data collected from 228 employees through scenario and survey methods. The Hayes PROCESS Macro was employed to examine the proposed hypotheses.

The results indicate a positive influence of WRC on EOB. Additionally, RI and EE sequentially mediate the relationship between WRC and EOB. Furthermore, PFC moderates the serial mediation process (RI and EE) between WRC and EOB as well as the adverse effects of WRC on RI.

Grounded in the theoretical framework of conservation of resource (COR) theory and cognitive-affective personality system (CAPS) theory, the present study offers new insights. By establishing the complicated interplay of RI and EE between WRC and EOB along with the moderating role of PFC, the study extends the understanding of the mechanisms involved, providing a more comprehensive perspective. By shedding light on these complicated interconnected links, the study paved the way for positive social dynamics at work.

Purpose: This study investigates the influence of inflation and oil price fluctuations on stock prices and returns. A thorough bibliometric analysis of research articles published in the last 22 years has been conducted to achieve this objective.

Need for the Study: Inflation and oil prices are critical macroeconomic variables that significantly impact the economy, including the stock market. It is crucial for investors and policymakers to understand the complex relationship between these economic variables and the stock market.

Methodology: The study employed a bibliometric analysis to identify the most influential authors, institutions, and research themes related to the impact of inflation and oil prices on stock prices.

Findings: The study identified that the fluctuations of the stock market are significantly influenced by both inflation and oil prices. This relationship has been extensively studied in the literature, with several key research themes identified. These themes include the effects of inflation and oil prices on various sectors and industries, the role played by monetary and fiscal policy, and the impact of international trade and global economic conditions.

Practical Implications: The insights that have emerged have significant implications for investors and policymakers. It highlights the need for risk management strategies and mitigation of macroeconomic factors’ impact on stock prices.

Generative artificial intelligence (GAI) can disrupt how consumers search for information on retail products/services online by reducing information overload. However, the risk associated with GAI is high, and its widespread adoption for product/service information search purposes is uncertain. This study examined psychological drivers that impact consumer adoption of GAI platforms for retail information search.

We conducted 31 in-depth, semi-structured interviews with the lead GAI users regarding product/service information search. The data were analysed using a grounded theory paradigm and thematic analysis.

Results show that consumers experience uncertainty about GAI’s functioning. Their trust in GAI impacts the adoption and usage of this technology for information search. GAI provides unique settings to investigate potential additional factors, leveraging UTAUT as a theoretical basis. This study identified three overarching themes – technology characteristics, technology readiness and information characteristics – as possible drivers of adoption.

Consumers seek exhaustive and reliable information for purchase decisions. Due to the abundance of online information, they experience information overload. GAI platforms reduce information overload by providing synthesized and customized product/service search results. However, its reliability, trustworthiness and accuracy have been questioned. The functioning of GAI is opaque; the popular technology adoption model such as UTAUT is general and is unlikely to explain in totality the adoption and usage of GAI. Hence, this research provides the adoption drivers for this unique technology context. It identifies the determinants/antecedents of relevant UTAUT variables and develops an integrated conceptual model explaining GAI adoption for retail information search.

The study aims to assess the impact of disruptive frugal digital technology and small and medium-sized enterprises' (SMEs') innovation performance on the food sector in Masvingo urban, Zimbabwe. A descriptive research design was used, and quantitative data were obtained using a questionnaire survey on 50 restaurant employees and 100 customers. The empirical findings demonstrated that the performance of SMEs was significantly improved by social media, cloud computing, virtual reality (VR), augmented reality (AR), and robotics. The study concludes that disruptive digital technologies (social media, cloud computing, AR, VR and robots affect the performance of SMEs by improving food production, streamlining information transfer, and utilizing cutting-edge technical applications. This chapter recommends that the government, through those in charge of formulating policy, educate and encourage the adoption of disruptive digital technology. The application of social media, cloud computing, VR, AR, and robotics will increase local food producers' access to the market. Digitalization will have a significant effect on Zimbabwe's local food system in the future.

This study aims to understand the kind of content and context that effectively create higher fan social media engagement (SME) through pre-match content posted by sports teams.

This research examines the effect of inspirational, informational, entertaining and warmth content appeal on affective and cognitive responses from fans in the form of likes and shares. Messages on X (previously Twitter), chosen as a representative social media platform, from the teams participating in the Indian Premier League, were analysed using regression models to validate the proposed model empirically.

For sports clubs, entertaining, warmth and inspirational content is more effective than information content in generating likes on social media. Content with high vividness is effective only for sports teams with high performance. Fans of low-performance teams exhibit higher responsiveness to content with inspirational appeal.

This research contributes to the sports marketing literature by examining the influential role of warmth and inspirational content in generating higher SME in the pre-match context.

This study provides prescriptions to sports clubs for leveraging social media platforms to engage their fans through appropriate content. Given the growth of sports leagues in developing and developed countries, this study provides guidelines to sports clubs for effective social media marketing.

To the best of the authors’ knowledge, this study is among the first to integrate social identity theory and elaboration likelihood model theoretical frameworks to study fan engagement with social media content posted by sports clubs.

This paper considers an e-tailer planning to distribute a product under one direct sales channel and multiple asymmetric agency platforms. Based on the multinomial logit (MNL) choice model, this study optimizes the pricing strategy and channel selection strategy to maximize the e-tailer’s profit.

A two-stage channel selection and pricing problem is formulated, where the profit-maximizing e-tailer first optimally selects a specified number of agency platforms from a set of alternatives to distribute the product and then determines the optimal prices in those channels.

An optimal pricing strategy is proposed to maximize the e-tailer’s total profit on multiple asymmetric channels. The results show that the e-tailer can obtain a higher profit by selling products on more asymmetric agency platforms. Moreover, an effective channel selection algorithm is provided to help the e-tailer optimally select the M agency platforms from N alternatives.

This study enriches the relevant research on multichannel selection and pricing by proposing an optimal pricing strategy and an effective channel selection algorithm. Evaluation results based on real-world industrial data show that the proposed optimal multichannel pricing strategy in this paper can significantly improve the profit of a real-world e-tailer compared to the e-tailer’s actual profit.

This study aims to comprehend the impact of omnichannel strategies and customer experience on brand engagement in the banking sector.

The self-determination theory (SDT) is applied to validate its effects on omnichannel perception, customer experience and brand engagement. A quantitative research approach was employed to gather comprehensive insights, using a structured questionnaire to survey 2029 bank users in five countries: Argentina, Brazil, Spain, Mexico and the United States.

Research confirms the positive impact of omnichannel perception and customer experience on brand engagement. Noteworthy patterns between Brazil and Argentina reveal similarities in brand engagement behavior, with Brazil standing out as a performer.

The study emphasizes the critical importance of integrating multiple channels to optimize user experience and increase brand engagement in the banking sector. The application of SDT provides a valuable framework for understanding the motivational factors influencing consumer behavior in omnichannel environments, contributing to the originality of the research.

The purpose of this study is to conduct a comprehensive review of ChatGPT in the education sector. By delving into the published literature, the research aims to uncover the benefits, drawbacks, present applications and prospective uses of ChatGPT for various stakeholders.

The research employs quantitative methodologies. Utilizing the Scopus database, the authors applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework to gather data. Additionally, the study includes a bibliometric analysis conducted through the VOSviewer visualization tool and R Studio to achieve the research objectives.

ChatGPT is making a transformative impact on the education sector. A thorough literature review revealed that ChatGPT has several benefits and drawbacks for students and educators. Additionally, the study sheds light on present applications of ChatGPT and explores its prospective uses for its key stakeholders.

PRISMA methodology in systematic reviews faces challenges in handling publication bias and evaluating study quality. Systematic reviews are limited by their inability to comprehensively cover all relevant research and depend on the quality of included studies. Bibliometric analyses may oversimplify research landscapes, neglecting qualitative insights. The research relies on existing literature, introducing potential biases due to varied accessibility. The study’s focus on the Scopus database and time constraints may exclude recent significant studies.

The study has several recommendations for educational institutions, students, educators, administrative staff and ChatGPT service providers. These recommendations collectively aim to provide comprehensive guidance to stakeholders, fostering an environment where ChatGPT can effectively transform the education sector.

This research conducts a comprehensive examination of ChatGPT in the education sector, with a primary emphasis on exploring its prospective uses for students, educators and administrative staff. By highlighting the potential benefits, the study aims to provide key stakeholders with opportunities to leverage ChatGPT for the transformation of the education sector.

This research explores the relationships between financial awareness and investor satisfaction about retirement planning, particularly in the Indian context. We developed a conceptual model involving double moderation effects of post-retirement objectives and external factors influencing investor satisfaction.

A carefully crafted survey instrument was designed to collect data from the 480 employees working in six administrative departments in the northern part of India. After checking the measurement properties of the survey instrument through the Lisrel package of structural equation modeling, the hypotheses were tested using Hayes PROCESS macros.

The results indicate that financial awareness is positively related to (1) investor satisfaction and (2) investment purpose; investment purpose is positively related to investor satisfaction; post-retirement objectives moderate the relationship between financial awareness and investment purpose; external factors moderate the moderated relationship between financial awareness and post-retirement objectives (first moderator) and investor satisfaction mediated through investment purpose.

The outcomes of this study have important implications for the employees embarking on making decisions concerning their retirement. The conceptual model provides a simple framework explaining how financial awareness and investor satisfaction are affected by the post-retirement objectives of individuals. This study highlights the importance of considering the effect of external factors influencing financial decisions.

The three-way interactions in this exploratory research contribute to the growing literature on behavioral finance, particularly concerning retirement planning involving pension.

This paper aims to explore the intersection of artificial intelligence (AI) and marketing within the context of knowledge management (KM). It investigates how AI technologies facilitate data-driven decision-making, enhance business communication, improve customer personalization, optimize marketing campaigns and boost overall marketing effectiveness.

This study uses a quantitative and systematic approach, integrating citation analysis, text mining and co-citation analysis to examine foundational research areas and the evolution of AI in marketing. This comprehensive analysis addresses the current gap in empirical investigations of AI’s influence on marketing and its future developments.

This study identifies three main perspectives that have shaped the foundation of AI in marketing: proxy, tool and ensemble views. It develops a managerially relevant conceptual framework that outlines future research directions and expands the boundaries of AI and marketing literature within the KM landscape.

This research proposes a conceptual model that integrates AI and marketing within the KM context, offering new research trajectories. This study provides a holistic view of how AI can enhance knowledge sharing, strategic planning and decision-making in marketing.