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The objective of this paper is to investigate the effect of working capital efficiency (WCE) and its components on the composite financial performance of a sample of Indian firms.

Our sample includes 796 non-financial listed firms from 2015–16 to 2021–22. Sample firms’ profitability, liquidity, solvency, cash flow management, and financial and operational leverage have been used to classify them into companies with high composite financial performance (HCFP) and with low composite financial performance (LCFP) by using K-Means Clustering technique. A composite financial performance score (CFPS) of 1 has been assigned to HCFP and 0 to LCFP. We have used logistic regression models with fixed effect to estimate the effect of cash conversion cycle (CCC) and its components, i.e. inventory days, accounts receivable days and accounts payable days on CFPS in the presence of control variables such as growth, leverage, firm size, and age.

The study finds that CCC and inventory days are inversely associated with CFPS. This finding shows that the firms’ WCE leads to superior financial performance on a composite basis.

The research findings are based on samples drawn from the population of the listed Indian non-financial companies. Since the operation, financial practices, working capital policies, and management styles of firms vary greatly among nations, the results of this study should be extended to firms in other countries after taking into account the degree of resemblance to the sample firms.

The findings of this study hold significant value for industry practitioners, as they provide guidance in determining the optimal allocation of funds for working capital and devising strategies for effectively managing inventory levels, credit sales, and vendor payments in order to increase the overall value of the company. This study aims to help investors in building their investment portfolios by identifying companies with superior composite financial performance. Investors can enhance the construction of their investment portfolios by strategically selecting companies that demonstrate superior overall performance.

The results of our study will help companies improve their WCM strategies to enhance their overall value, and their significance increases manifold during economic downturns. Business firms that perform well by efficiently managing their working capital have a multiplier effect on the economy and society at large in the form of GDP contribution, labor income, taxes to the government, investment in capital assets, and payments to suppliers.

To understand the impact of WCE on firms’ performance, the extant working capital literature focuses on some specific characteristics such as profitability, valuation, solvency, and liquidity. The limitation of employing a single parameter is its inability to present the comprehensive performance evaluation of firms. This study is among the earliest studies that focus on the holistic evaluation of WCE's impact on the composite performance of a company.

Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.

The purpose of this paper is to examine customers’ behavioural outcomes based on the actual attitudinal responses of mobile marketing initiatives.

A total of 764 usable responses was included through a non-probability convenient sampling method. The data used in the study gathered from mobile users of 37 cities from nine states across India. The analysis of moment structures 22.0 and SmartPLS 3.0 statistical programmes were used for measurement validation and to test the structural model.

The study indicated that mobile marketing had a more significant effect on customers’ negative attitudes and followed by on positive attitudes. Despite the strong significant effect on customers’ negative attitudes, it does not display more significant effect on negative behavioural outcomes. Finally, the study suggested that customers’ positive attitudes display more favourable behavioural outcomes of mobile marketing initiatives.

The paper would help the mobile marketers and advertisers to understand the impact of mobile marketing initiatives on customers’ attitudes and behavioural outcomes and how it can be managed to ensure the higher level of adoption and acceptance.

The results of the analysis indicated that when the users have favourable attitudes of mobile marketing initiatives, it can be highly effective in triggering of favourable behavioural outcomes.

The purpose of this paper is to develop a structural framework for factors influencing the creation of negative electronic word of mouth (WOM) at online forums, review portals, social networking sites or any other internet-based platform. Consumers frequently talk about consumption through WOM. With the unprecedented progress in digitalization of world, consumers usually tell stories about a product or service experience online. The study aims to identify the antecedents for negative electronic WOM (EWOM) and develop a structural model with different levels of factors.

Literature review and group judgment techniques are used to identify the factors responsible for negative WOM. The study uses interpretive structural modeling approach to develop a structural model representing interrelationships among factors. The model also represents the factors at different levels along with most and least dominant factors responsible for EWOM.

Seven factors responsible for negative electronic WOM are identified which are spread over three levels. First level of factors includes anxiety reduction, advice seeking, exertion of power (over companies) and social benefits (received), whereas the second level of factors includes altruism (negative WOM) and economic rewards. The most dominant factor identified is vengeance, which is at level three.

The study identified three levels of factors responsible for negative EWOM. Companies can strategies their resources to ensure that no consumer reaches beyond level one. Beyond level one, the involved factors may jeopardize the brand value and brand image of the company.

The study contributes to the literature by providing a structural model for negative EWOM. This model identifies the most and least dominant factors and guides managers to take necessary action to prevent negative EWOM.

Introduction: Artificial intelligence (AI) and digitisation offer substantial human potential and profit margins, making them promising retail solutions. Retail leaders have successfully integrated comprehensive uses into their daily operations, while competitors heavily invest in new projects. The Indian retail sector is undergoing a significant transformation, which can be attributed to factors such as growing income, demographic characteristics, and enhanced consumerism, as well as the rapid development of new technologies such as digitisation and AI, which is changing both consumers’ and retailers’ buying behaviour.

Purpose: This study aims to determine the influence of AI on elements that drive digitisation in the retailing sector, as well as the factors that lead to organised retailers adopting digitisation and its impact on their business.

Methodology: The study employs a standardised questionnaire distributed to organised stores via an online link, and the data are analysed with SmartPLS software 3.0.

Finding: The retail sector is driven by elements that promote digitalisation in food and groceries retailing, such as simplicity of operation, adoption of digital payment, quicker internet connection, retailer consumer interface, and the involvement of AI.

Research implication: AI has significant consequences for retailing, which serves as the interface between marketers and customers.

Theoretical implication: The study’s findings reflect the perspectives of retailers, store managers, and entrepreneurs on how digitalisation and AI are crucial for the creation and growth of long-term competitive advantages in retail.

The COVID-19 pandemic has recently had an impact on the stock market all over the globe. A thorough review of the literature that included the most cited articles and articles from well-known databases revealed that earlier research in the field had not specifically addressed how the BRIC stock markets responded to the COVID-19 pandemic. The data regarding COVID-19 were collected from the World Health Organization (WHO) website, and the stock market data were collected from Yahoo Finance and the respective country’s stock exchange. A random forest regression algorithm takes the closing price of respective stock indices as target variables and COVID-19 variables as input variables. Using this algorithm, a model is fit to the data and is visualised using line plots. This study’s findings highlight a relationship between the COVID-19 variables and stock market indices. In addition, the stock market of BRIC countries showed a high correlation, especially with the Shanghai Composite Stock Index with a correlation value of 0.7 and above. Brazil took the worst hit in the studied duration by declining approximately 45.99%, followed by India by 37.76%. Finally, the data set’s model fit, which employed the random forest machine learning method, produced R² values of 0.972, 0.005, 0.997, and 0.983 and mean percentage errors of 1.4, 0.8, 0.9, and 0.8 for Brazil, Russia, India, and China (BRIC), respectively. Even now, two years after the coronavirus pandemic started, the Brazilian stock index has not yet returned to its pre-pandemic level.

The purpose of this paper is to provide insights into the benefits of data‐driven services marketing and provide a conceptual framework for how to link traditional and new sources of customer data and their metrics. Linking data and metrics to strategic and tactical business insights and integrating a variety of metrics into a forward‐looking dashboard to measure marketing ROI and guide future marketing spend is explored.

A detailed synthesis of the literature is conducted and contemporary sources of marketing data are categorized into traditional, digital and neurophysiological. The benefits and drawbacks of each data type are described and advantages of integrating different sources of data are proposed.

The findings point to the importance and untapped potential of data in its ability to inform tactical and strategic marketing decisions. Future challenges, including top management support, ethical considerations and developing data and analytic capabilities, are discussed.

The results demonstrate the need for executive service marketing dashboards that include key metrics that are service‐relevant, complementary and forward‐looking, with proven linkages to business outcomes.

This paper provides a synthesis of data‐driven services marketing and the value of traditional and contemporary metrics. Since the true potential of data‐driven service management in a connected world is still largely unexplored, this paper also delineates fruitful avenues for future research.

The primary objective of the present study is to explore the relationship between workplace spirituality and work stress among offshore and onshore employees of the Indian oil and gas industry. The present study also tends to study the difference in the stress level of offshore and onshore employees of the Oil and Gas Industry.

The size of the sample for the present study was 202 respondents. It includes 128 onshore employees and 74 offshore employees of oil and gas companies. Respondents were mainly managers and supervisors working in various departments of Oil and Natural Gas Corporation (ONGC), Cairn India, Reliance India Ltd (RIL), Bharat Petroleum Corporation Ltd (BPCL) and Indian Oil and Gas Ltd (IOCL). Since the different level of stress is experienced by employees at different stages of the organizational structure, thus study selected population comprising of managers and supervisors since they are believed to face similar work stressors. A variety of statistical tools like mean, t-test, correlation and multi-regression is used for the analysis of collected data.

Results show that all six dimensions of workplace spirituality are significantly negatively correlated with stress for onshore employees. However, the sense of community and gratitude are found insignificantly associated with stress for offshore employees. Stressful offshore conditions and excessive specialization might not allow offshore employees to cherish the community at the workplace and also the virtue of gratefulness. The offshore employees might have a certain level of gratitude and community system, but it is not sufficient for the employees to perceive a lower level of work relates to stress. The result gives the impression that the normal working conditions (onshore workplace) provide adequate opportunity to workplace spirituality to transcend its impact on work stress.

This is one of the pioneer studies that examined the role of workplace spirituality and stress in stress management of offshore and onshore employees of Indian Oil and gas companies.