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The aim of this study was to develop dietary fibre-enriched chicken sausage by incorporating fibre from a by-product of corn milling, apple and tomato processing.

Sausages were developed by replacing lean meat with corn bran (CB), dried apple pomace (DAP) and dried tomato pomace (DTP) each at 3, 6 and 9 per cent levels. Organoleptic, nutritional and physico-chemical quality attributes of treated sausages were evaluated. One product from each fibre source with very good organoleptic acceptability was selected to estimate total dietary fibre content and assess shelf life under refrigerated temperature.

Organoleptic acceptability of 3 per cent fibre-incorporated sausages were comparable with control. Moisture content decreased significantly in all treated sausages, protein content decreased in CB- and DAP-treated sausages, while ash content increased significantly in DTP-treated sausages. Emulsion stability and cooking yield was significantly higher in 6 and 9 per cent treated sausages, while crude fibre content was significantly higher in all the treated sausages.

Chicken sausages with very good acceptability, higher dietary fibre content and storability up to 15 days at refrigerated temperature were developed by incorporating CB at 3 per cent level and DAP and DTP each at the 6 per cent level.

The research is of value to meat processors. By-products like corn bran, apple and tomato pomace which are of low value can be profitably utilized to develop fibre enriched chicken sausage. Developed products will also help in promoting the image of meat as a healthy food.

Mahindra Trucks and Bus Division (MTBD) of Mahindra & Mahindra is at an interesting stage of its evolution. Having gone through a bad patch with a product that was not quite up to the mark, it appears to have got the product right by early 2018 and truck sales had been going up in the country for the previous four years. While Mahindra & Mahindra as a company is a large firm with revenues of more than USD 15 billion, MTBD itself is a small player (INR 2400 crore, USD 350 million) within the firm and in an Indian truck industry that is dominated by goliaths, Tata Motors and Ashok Leyland that between them had more than 81% market share and a customer mindset that was loath to leave the comfort of a known brand. The case provides data on product specifications, prices, marketing communication, channels, positioning, the context and competition. The intention is to use the case to go through the steps involved in evaluating and developing and creating a marketing plan for MTBD to increase its market share from the 4% in 2018 to an intended 8% in 2022.

This study aims to identify and assess the key ethical challenges associated with integrating artificial intelligence (AI) in knowledge-sharing (KS) practices and their implications for decision-making (DM) processes within organisations.

The study employs a mixed-methods approach, beginning with a comprehensive literature review to extract background information on AI and KS and to identify potential ethical challenges. Subsequently, a confirmatory factor analysis (CFA) is conducted using data collected from individuals employed in business settings to validate the challenges identified in the literature and assess their impact on DM processes.

The findings reveal that challenges related to privacy and data protection, bias and fairness and transparency and explainability are particularly significant in DM. Moreover, challenges related to accountability and responsibility and the impact of AI on employment also show relatively high coefficients, highlighting their importance in the DM process. In contrast, challenges such as intellectual property and ownership, algorithmic manipulation and global governance and regulation are found to be less central to the DM process.

This research contributes to the ongoing discourse on the ethical challenges of AI in knowledge management (KM) and DM within organisations. By providing insights and recommendations for researchers, managers and policymakers, the study emphasises the need for a holistic and collaborative approach to harness the benefits of AI technologies whilst mitigating their associated risks.

Research on the Internet of Things (IoT) has gained momentum in various industry contexts. However, the literature lacks broad empirical evidence on the factors that influence users' intention to adopt this cutting-edge technology, especially in the food and beverage industry (F&BI) – a significant yet unexplored setting. Therefore, the authors aim to extend the “Unified Theory of Acceptance and Use of Technology (UTAUT)” model by coupling it with perceived collaborative advantage, organizational inertia and perceived cost and explore the key determinants of IoT adoption for the digital transformation of the F&BI.

This study employs a cross-sectional quantitative approach, where a sample of 307 usable responses was drawn from the senior managers of the Australian F&BI.

The authors have found that performance expectancy, perceived collaborative advantage, effort expectancy, social influence and facilitating conditions have a strong positive influence on the behavioural intention to adopt IoT for the digital transformation of the F&BI. Furthermore, while high perceived costs and organizational inertia are often considered negative factors in adopting new technology, our results reveal the insignificant influence of these factors on the adoption of IoT, which is interesting. The findings also suggest that age and voluntariness significantly moderate most of the relationships, while gender is an insignificant moderator.

The study provides several novel insights into the existing body of knowledge by extending the UTAUT model with three variables and applying it in a unique context.

The importance of work design to organizational engagement and firm performance is increasingly recognized in management scholarship. For international business, a majority of variation in work design based on national cultures is addressed through cross-cultural management scholarship. However, there is a paucity of qualitative research on the influences international business human resource managers face for work design in the intercultural environment of overseas subsidiaries. The purpose of this interpretivist study was to examine the lived experience of overseas subsidiaries’ local managers to surface a more nuanced understanding of their expectations and related implications for work.

Empirical research was conducted through semistructured in-depth interviews with senior managers of subsidiaries of Japanese MNCs in USA, Thailand and India.

The findings of the study develop and extend on prior cross-cultural management scholarship on world cultural clusters revealing changed expectations of work in intercultural work environments as instantiated by Japanese MNCs.

Through engaging work design, international businesses can contribute to the United Nations Sustainable Development Goal 8 that pertains to decent work.

The study adds to extant understanding of the work design antecedent to engagement by broadening to intercultural environment impacts understanding facilitated by empirical lived experience data and suggesting a modification to extant theory. This study pioneers in taking world cultural clusters as the field for evaluating data.

This study seeks to comprehend if political exposure containing disapproval and different values will affect implicit knowledge sharing (KS) amongst colleagues in the organization. This research examines participants' responses to a colleague's social-media political exposure and their readiness to perform implicit KS to their colleague.

Data collection uses an online questionnaire and a vignette approach. Subsequently, data analysis for 316 finished surveys employs structural equation modelling-partial least squares (SEM-PLS).

The findings show that the perceived-value similarity of political posts of a colleague significantly and indirectly affects workers' readiness to do implicit KS. Besides, likes and trusts also significantly affect workers' readiness to perform implicit KS. While perceived-value similarity strongly shapes likes, likes significantly and positively affect trusts.

Sharing social-media postings associated with political exposure can hinder the implicit KS in organizations and is understudied in the field of knowledge management. Especially, unlike this study which focuses on private companies, previous studies have paid more attention to public enterprises. Besides, this paper's empirical verification is obtained from private organizations in Indonesia, which is also neglected by scholars.

In the present era of Industry 4.0, the manufacturing automation is moving toward mass production and mass customization through human–robot collaboration. The purpose of this paper is to describe various human–robot collaborative (HRC) techniques and their applicability for various manufacturing methods along with key challenges.

Numerous recent relevant research literature has been analyzed, and various human–robot interaction methods have been identified, and detailed discussions are made on one- and two-way human–robot collaboration.

The challenges in implementing human–robot collaboration for various manufacturing process and the challenges in one- and two-way collaboration between human and robot are found and discussed.

The authors have attempted to classify the HRC techniques and demonstrated the challenges in different modes.

Mahindra Trucks and Buses forayed into India's commercial vehicles sector in 2005. However, they had to battle numerous supply chain challenges associated with introducing a new product (a new truck brand) in the market and to gain a noticeable foothold in the market. In this case, we attempt to align customer brand stickiness with the supply chain expectations from a new product. In particular, we deliberate how the needs of all actors in the supply chain must be met and their interactions must be accounted in developing a robust supply chain. Finally, a supply chain is successful when demand can be matched with supply and the customer's service level can be achieved.

This chapter focuses on a case study of Kareem Exchange Company and its use of big data analysis to detect and prevent fraud and suspicious financial transactions. The chapter describes the various phases of the big data analysis cycle, including discovery, data preparation, model planning, model building, operationalization, and communicating results, and how the Kareem Exchange Company team implemented each phase. This chapter emphasizes the importance of identifying the business problem, understanding the resources and stakeholders involved, and developing an initial hypothesis to guide the analysis. The case study results demonstrate the potential of big data analysis to improve fraud detection capabilities in financial institutions, leading to informed decision making and action.

The study's objective is to examine the consumers' intention to buy counterfeit brand shoes. A conceptual model is developed to test the risk-taking and word-of-mouth (WOM) as a moderator in the relationship between status consumption, brand image, and consumer intention to buy counterfeit shoes.

Based on the theory of reasoned action (TRA) and signaling theory (ST), this research was conducted in the Indian National Capital Region. Using a structured instrument, the data was collected from 240 respondents. After checking the psychometric properties of the survey instrument using the Lisrel package of structural equation modeling, Hayes's PROCESS macros were used for testing the hypotheses.

The findings from the study indicate that (1) status consumption and brand image are positively associated with purchase intention of counterfeit brand shoes, and (2) risk-taking moderates the relationship between (1) status consumption and purchase intention, and (2) brand image and purchase intension, (3) significant three-way interaction between WOM, risk-taking and status consumption on purchase intention, and (4) significant three-way interaction between brand image, WOM, and risk-taking on purchase intention of counterfeit brand shoes.

As with any survey research, this study has common method variance as a potential problem. However, through the latent variable method and Harman's single-factor analysis, the common method variance was checked. The study has several implications for managers, e-marketers, and consumers.

The study has several implications for marketers selling counterfeit products and managers intending to protect their branded products.

A conceptual model showing two-way and three-way interactions between status consumption, risk-taking, and WOM influencing the consumer purchase intention of counterfeit products was discussed. This is the first of its kind in India to explore such relationships.