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Kids get exposed to advertising on social media platforms when they visit them to perform various goals. The purpose of this study is to find out the factors which affect kids' behaviour when the kids encounter advertising on social media and also to establish causal relationships amongst the factors of social media advertising (SMA).

A total of 11 factors of SMA have been identified with the help of experts and the causal relationships amongst the SMA factors have been constructed by the implementation of decision-making trail and laboratory evaluation (DEMATEL). Based on the established relationships, a causal diagram has been also developed to understand the structural nature of interdependence amongst the factors.

DEMATEL technique is based on logical steps, which have assisted in categorising the identified factors into two groups: cause group and effect group. Cause group factors are the reasons for the effect group factors to occur. Customisation, entertainment, information and interactivity have been observed as cause factors whereas, relevance, engagement with SMA, purchase intention, product involvement, advertising value, attitude towards SMA and irritation have been observed as effect factors. Product involvement is found to have the highest level of interaction with all other factors. Information and interactivity are observed to influence all other factors.

Kids possess a limited understanding of the selling intent of advertisers which makes kids vulnerable to advertising. This study supports that the content of the advertisement should be kept in accordance with the need of kids and also suggests that marketers should emphasise cause group factors which derive subsequent consequences on effect group factors. The foremost limitation of this study lies in the process of identifying the factors through expert opinions. The sets of contextual relationships may vary when different experts are considered.

This study strives to identify the factors which affect kids' understanding of SMA and also establishes causal relationships amongst them. This kind of study is unique in state of the art and to the authors' knowledge no significant research has been conducted in India which involves establishment of inter-relationships amongst SMA factors that affect kids' behaviour.

Interest rate derivatives (IRDs) are the essential components of financial risk management and are used across various industry sectors. The objective is to analyze the differences in approach to managing interest rate risks between the Indian corporates that execute IRDs and the ones that do not.

Interest rate fluctuations require Indian corporates to hedge their exposures in foreign currency interest rates. This is all the more true for mid-sized corporates because of their balance sheet exposures. Additionally, they may not have the resources to formulate risk management policies. This paper analyzes data collected from financial statements of a diverse set of companies that use IRD and helps in formulating such a strategy.

The results indicate significant differences for some of the parameters like information asymmetry and agency cost between users and non-users of IRDs. The study further suggests causality between users of derivatives and parameters like size, growth and debt.

The study compares users and non-users of IRDs, thereby identifying factors unique to users of IRDs. It also studies causality relations which explain the motivation to do IRDs. Thus, it enables risk managers to use this as a reference point to decide on their strategies.

While there are multiple studies across geographies and sectors including commercial banks in India on the usage of interest rate swaps, this study focuses on Indian mid-tier corporates. Furthermore, the study distinguishes between users and non-users based on financial parameters, which in turn would provide a framework for decision-hedging strategies.

New business practices and the globalization of markets force firms to take innovation as the fundamental pillar of their competitive strategy. Research and Development (R&D) plays a vital role in innovation. As technology advances and product life cycles become shorter, firms rely on R&D as a strategy to invigorate innovation. R&D project portfolio selection is a complex and challenging task. Despite the management's efforts to implement the best project portfolio selection practices, many projects continue to fail or miss their target. The problem is that selecting R&D projects requires a deep understanding of strategic vision and technical capabilities. However, many decision-makers lack technological insight or strategic vision. This article aims to provide a method to capitalize on the expertise of R&D professionals to assist managers in making informed and effective decisions. It also provides a framework for aligning the portfolio of R&D projects with the organizational vision and mission.

This article proposes a new strategic approach for R&D project portfolio selection using efficiency-uncertainty maps.

The proposed strategy plane helps decision-makers align R&D project portfolios with their strategies to combine a strategic view and numerical analysis in this research. The proposed strategy plane consists of four areas: Exploitation Zone, Challenge Zone, Desperation Zone and Discretion Zone. Mapping the project into this strategic plane would help decision-makers align their project portfolio according to the corporate perspectives.

The new approach combines the efficiency and uncertainty dimensions in portfolio selection into an integrated framework that: (i) provides a complete representation of the stochastic decision-making processes, (ii) models the endogenous uncertainty inherent in the project selection process and (iii) proposes a computationally practical and visually unique solution procedure for classifying desirable and undesirable R&D projects.

Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.

The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.

The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.

In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.

The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).

This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

This study investigates the quantitative aspect of the various strains of operational excellence (OE) and competitive-potential (CP) in the SME sector. It has five steps, i.e., identifying the key performance constructs of OE and their hypothesized relationship pattern from literature, validating these constructs through factor analysis, formalizing their empirical relationships by structural-equation-modeling (SEM), path analysis of performance constructs with the empirical results, and lastly proposing a framework for OE deployment in SMEs.

Data for the deployment scores of operational excellence procedures (OEPs) were collected through a structured questionnaire survey. Nine hundred participants from a stratified random sample were approached for the survey, and 473 responses were received. Sample stratification was based on Gender, Education, Experience, Position, Department and Industry. Respondents had 5–30 years of experience managing manufacturing operations, holding the manager position and above.

The path analysis of the structural model provides unique insights into OE's practical aspects in SMEs (small and medium enterprises). For example, Contractual-conformance and Process-efficiency play pivotal roles as both have a significant positive impact on CP. Supplier efficacy, Consistency and Product-excellence do not improve CP unless mediated by Contractual-conformance or Process-efficiency.

The study provides important implications for academia, policymakers and managers. The study identifies and validates the operational excellence key performance practices and proposes a framework for manufacturing organizations. SME managers can follow the framework to develop effective operational excellence strategies to help them achieve their organizational goals. Additionally, the study emphasizes the need for continuous culture in SMEs, which will help to support operational excellence deployment. Overall, the implications presented in the study will help SMEs to enhance their competitiveness and operational performance.

The study explores the empirical investigation of the operational excellence deployment in SMEs. The study uses a mixed method approach for research design, including qualitative and quantitative approaches, and uses SEM to test the proposed framework. Validation of OE's six key performance constructs and establishing their empirical relation is an attempt to advance the Operations excellence theory. Unlike large enterprises, SMEs demonstrate an incohesive response to the practices pertaining to Supplier efficacy, Consistency and Product-excellence. This unique response pattern requires special treatment, which is incorporated into the proposed framework.

COVID-19 pandemic has exposed that even the best of the developed nations have surrendered to the devastations imposed on the global supply chains. The purpose of this study is to explore how COVID-19 has exaggerated the supply chain of production and distribution of Taiwan-based face masks and also investigate the conscientious factors and subfactors for it.

In this study, an analytical hierarchy processes (AHP)-based approach has been used to assign the criterion weights and to prioritize the responsible factors. Initially, based on 26 decision-makers, successful factors were categorized into five main categories, and then main categories and their subcategories factors were prioritized through individual and group decision-maker’s contexts by using the AHP approach.

The results of this AHP model suggest that “Safety” is the most important and top-ranked factor, followed by production, price, work environment and distribution. The key informers in this study are stakeholders which consist of managers, volunteers, associations and non-governmental organizations. The results showed that good behavior of the employees under the “Safety” category is the top positioned responsible factor for successful production and distribution of face masks to the other countries with the highest global percentage of 15.7% and using sanitizers to protect health is the second most successful factor with the global percentage of 11.7%.

The limitations faced in this study were limited to only Taiwan-based mask manufacturing companies, and it was dependent on the decisions of the limited company’s decision-makers.

The novelty of this study is that the empirical analysis of this study has been based on a successful Taiwan masks manufacturing company and evaluates the responsible factors for the production and distribution of Taiwan masks to other countries during COVID-19.

The study aims to look into the mechanism by which perceived human resource management (HRM) practices impact nurses' engagement, by specifically looking into the role of psychological availability and psychological safety.

A cross-sectional questionnaire survey was conducted among nurses (n = 465). Data were collected from nurses of National Accreditation Board for Hospitals and Healthcare Providers (NABH) accredited hospitals by employing two stage sampling.

Results indicate significant positive association between HRM practices and employee engagement. Role of psychological safety and psychological availability as mediators was also confirmed. The study supported the proposition that HRM practices affected employee engagement through psychological safety and then psychological availability thus approving serial mediation.

This research also contributes to a more comprehensive understanding of the ways to achieve employees' psychological safety, availability, and thus nurse engagement.

Key enabling technologies (KETs) are a set of six technological components that work together to address social challenges and build advanced for sustainable economies. Industry 5.0, the next industrial development, is designed to capitalize on specialists' unique creativity while also collaborating with powerful, intelligent, and precise technologies. Industry 5.0 outsourced repetitive and monotonous activities to robots/machines requiring employees to perform activities that involve critical thinking and are based on the 6R (Recognize, Reconsider, Realize, Reduce, Reuse, and Recycle), to improve production quality. With numerous supporting technical advancements, advanced and quick manufacturing concentrating on the interaction of machines and humans may be produced. Maintaining healthcare and nursing care, evaluating patients' health requirements using KETs, and giving care with manpower are all major advancements in Industry 5.0 today. Future studies may focus on providing healthcare using mainly technology and, therefore, no human workers. This chapter highlights healthcare advances in Industry 5.0, where KETs and people collaborate to create and innovate. In this framework, the purpose of this chapter is to present the deployment of KETs in the nursing patient care process.

The study aims to identify organizational variables of quality 4.0 for an Indian manufacturing company in the case of digital transformation. Furthermore, the organization enhances its quality 4.0 performances to its success based on the degree of relevance of these variables, insight into these variables and sub-factors to prioritize them.

Initially, two rounds of the survey were conducted with 11 decision-makers from the company made to receive organizational variables scores and prioritize the factors and sub-factors. Analytic Hierarchy Process (AHP) based research methodology has been proposed to assign the criterion weights and prioritize the identified variables.

The results of this AHP model demonstrate that “Committed Leadership” is recognized as the top positioned variable and most significant organizational variable, followed by Collaboration and Quality culture, which are developed at the next level. These essential organizational variables with their sub-categories' priorities are identified as contributing attributes.

The findings facilitate quality 4.0 in the digitalization era, which take into contemplating the current state of the business. Furthermore, the understanding of variables provides insightful guidance to analyze, solve complex problems and assess the efficacy of quality 4.0 in digital transformation.

The novelty of this study is to pinpoint, and evaluate the responsible organizational variables and prioritize them that lead to high productivity and competitive advantage considering the AHP method.

With an industry 5.0 revolution taking place in the hospitality industry, a shift from manual to cognitive labor is anticipated, characterized by greater sustainability, resilience and a human-centric approach. In this regard, hospitality educators' ability and willingness to teach novel topics such as automation at work, upskilling of employees, man-machine interaction and service robots have become more important than ever. This study aims to interpret the perspectives of hospitality educators about bridging the gap in the employability skills of (next-gen) hospitality graduates and the concerns relating to career sustainability in times of transition.

A case study method was used given the novelty of the topic in a developing country like India. A qualitative survey with open-ended questions, is employed to understand the viewpoints of Indian hospitality educators, including those with more than 15 years of teaching experience. In-depth interviews were conducted with 23 hospitality educators to reach the theoretical saturation point. MAXQDA software was used to analyze the qualitative data collected in the study.

The findings reveal the challenges and motivations of hospitality educators in adapting to frequently changing business environments. In doing so, it sheds light on the methods employed to create a generation of hospitality graduates aligned with the changing dynamics of the industry.

The paper presents the viewpoints of hospitality educators in India in relation to a futuristic approach to next-gen employability and career sustainability. Whilst numerous studies have focused on the role of robots and artificial intelligence in replacing the human component of the service environment, the concept of people working alongside advanced technologies is fairly new and needs to be fully explored.