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Introduction: Historically, the corporate governance (CG) framework was designed primarily to safeguard the economic interests of shareholders, as a result of political and legal interventions, developing into an effective instrument for stakeholders and society in general.

Purpose: The core objectives of the study include: identifying journals/publications responsible for publishing CG studies in India, key CG issues covered by CG researchers, the amount of high-impact CG literature across different time periods, sectors/industries covered by CG researchers and different research instruments (quantitative or qualitative) used in CG studies in India.

Design/methodology: The chapter used a sample of 130 corporate governance studies that fulfil the selection criteria, drawn from the repository of over 100 reputed journals that are either recognised by the Australian Business Deans Council (ABDC) or indexed by SCOPUS. A systematic literature review has been carried out pertaining to CG issues in India, based on various statistical tools, data, industries, research outlets & citations, etc.

Findings: The results show an overwhelming number of studies have assessed the relationship between CG variables and firm performance, which could be measured through a variety of performance metrics such as ROA and ROI. Apart from empirical analysis, many conceptual studies use repetitive basic statistical tools like descriptive statistics or regression analysis. The chapter offers insights into current achievements and future development.

Originality/value: This bibliometric study is a useful guide for policymakers, corporate leaders, research organisations and management faculty to draw insights from work produced by eminent researchers in GC in India.

Introduction: Micro, small, and medium enterprises (MSME) have long been viable in the Indian economy. In the case of post-COVID-19, 20–40% of MSMEs in government can be permanently closed. The state should pay special attention to MSMEs for survival (Min, 2023).

Purpose: This chapter provides a framework for MSMEs to study industry challenges in Punjab and to discuss the conceptual framework and road map for future MSMEs in Punjab.

Need for This Study: The COVID-19 pandemic has drastically impacted the variable economic activities within the world. This study is responsible for explaining the different vulnerable sectors related to small- and medium-sized enterprises. On the other hand, this study is an analytical and descriptive research in nature.

Methodology: A mixed method of data collection has been used in this chapter. The data have been collected by floating a questionnaire to the various entrepreneurs of MSMEs. Secondary data have been collected through the Internet.

Findings: Through this research, we could analyse the MSMEs’ conceptual framework, the challenges they face, and the industrial units’ future roadmap.

Practical Implications: This research is mainly considered a clear explanation of current competition and market access challenges that small- and medium-sized enterprises face. This situation is derived due to the COVID-19 pandemic, so many enterprises are trying to find their exit ways. On the other hand, some MSMEs are trying to focus on the online business market to make some profit and to overcome the loss.

This study examines the dynamic relationships of visit behavior in the multiple channels [personal computer (PC) and mobile channels] on online store sales performance.

The empirical data were from an online store for the period between August 14, 2015 and May 15, 2016. The data consisted of consumer visit behavior and online store sales performance. Vector autoregression with an exogenous variables model was adopted to investigate the dynamic relationships.

The empirical results show significant relationships between visit behavior metrics (number of visitors, average number of visits per visitor and average length of each visit) in the two channels and online store sales performance. The number of visitors through the PC and mobile channels strongly and positively affects online store sales performance both in the short term and in the longer term. Moreover, the number of visitors in the PC channel has the strongest influence on sales performance metrics, followed by the number of visitors and the average number of visits in the mobile channel. The PC channel's visit behavior metrics explain a larger proportion of the sales performance variance than that in the mobile channel.

The previous literature on consumer behavior in multichannel marketing mainly focuses on channel selection or migration, and examines the different factors affecting channel choice behavior. Little is known about the impacts of visit behavior in the multiple channels. This study adopts the heuristic-systematic information processing theory to unveil the impacts of visit behavior metrics in the PC and mobile channels on online store sales performance.

This study investigates the profound impact of artificial intelligence (AI) capabilities on decision-making processes and organizational performance, addressing a crucial gap in the literature by exploring the mediating role of decision-making speed and quality.

Drawing upon resource-based theory and prior research, this study constructs a comprehensive model and hypotheses to illuminate the influence of AI capabilities within organizations on decision-making speed, decision quality, and, ultimately, organizational performance. A dataset comprising 230 responses from diverse organizations forms the basis of the analysis, with the study employing a partial least squares structural equation model (PLS-SEM) for robust data examination.

The results demonstrate the pivotal role of AI capabilities in shaping organizational decision-making processes and performance. AI capability significantly and positively affects decision-making speed, decision quality, and overall organizational performance. Notably, decision-making speed is a critical factor contributing significantly to enhanced organizational performance. The study further uncovered partial mediation effects, suggesting that decision-making processes partially mediate the relationship between AI capabilities and organizational performance through decision-making speed.

This study contributes to the existing body of literature by providing empirical evidence of the multifaceted impact of AI capabilities on organizational decision-making and performance. Elucidating the mediating role of decision-making processes advances our understanding of the complex mechanisms through which AI capabilities drive organizational success.

The “war for talent” is not limited to developed economies but has become a common feature in emerging economies such as India. From the sociocultural perspective, India represents one of the oldest cultural heritages with distinct cultural values. The cultural difference may contribute to explain organizational practices toward talent retention. In the present chapter, the authors focus on the institutional, legal, and cultural context and highlight their uniqueness with respect to the Indian context. Within the institutional context, the authors found that prior to liberalization (which happened in 1990s), the Indian business scene was dominated by public firms or a small enclave of private firms. For both types of organization, turnover hardly mattered, and turnover was indeed negligible. Employees saw firms as “employers for life”: in such a context, voluntary turnover was extremely rare. Further, in the early legal context, it was hard for any private firm to “fire” an employee. Therefore, involuntary turnover was close to nil as well. Things began to change post-liberalization when the Indian scene was dominated by an influx of private players. The Indian mind too accepted turnover to be a part of the corporate life. In the present chapter, the authors provide a snapshot of what, why, and how of employee turnover in the Indian context. The authors specifically focus on what motivates employees to remain with the organization or why do they leave the organization. The authors close the chapter with insights relevant to both academicians and practitioners.

This article has been withdrawn as it was published elsewhere and accidentally duplicated. The original article can be seen here: 10.1108/08876049710168519. When citing the article, please cite: Jeffrey Strieter, Ashok K. Gupta, S.P. Raj, David Wilemon, (1997), “Product management and the marketing of financial services”, Journal of Services Marketing, Vol. 11 Iss: 2, pp. 128 - 149.

In the academic field of business management, several potential theories were established during the last decades to explain companies' decisions, organizational behavior, consumer patterns, and internationalization, among others. As a result, businesses and scholars were able to analyze and decide based on theoretical approaches to explain the current conditions of the market. Secondary research was conducted to collect more than 36 management theories. This chapter aims to develop the most famous theories related to business applied in the international field. The novelty of this chapter relies on the compilation of recognized previous research studies from the academic literature and evidence in international business.

This study explores the potential of artificial intelligence (AI) in fostering sustainable entrepreneurship within the tourism industry. The rapid growth of the tourism sector has raised concerns regarding its environmental impact, social equity and economic sustainability. Sustainable entrepreneurship offers a promising approach to address these challenges by integrating environmental, social and economic considerations into business practices. AI technologies, with their ability to process vast amounts of data, analyse patterns and make predictions, have the potential to support sustainable entrepreneurship initiatives in the tourism industry. By analysing the current literature, this study provides insights into the effective utilisation of AI to promote sustainable entrepreneurship in the tourism industry, while acknowledging the need for responsible and ethical AI implementation. The findings contribute to the understanding of how AI can be harnessed as a tool for driving sustainable practices and innovation in the tourism sector, ultimately leading to a more sustainable and responsible tourism industry.