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Quantitative objective studies on the problem of celebrity selection are lacking. Furthermore, existing research does not recognize the group decision-making nature and the possibility of customer involvement in celebrity or influencer selection for social media marketing. This study conceptualizes celebrity selection as a multi-attribute group decision-making problem while deriving the final ranking of celebrities/influencers using interactive and flexible criteria based on the value tradeoff approach. The article thus proposes and demonstrates a quantitative objective method of celebrity selection for a brand or campaign in an interactive manner incorporating customer's preferences as well.

Each decision-maker's preferences for celebrity selection criteria are objectively captured and converted into an overall group preference using a modified generalized fuzzy evaluation method (MGFEM). The final ranking of celebrities is then derived from an interactive and criteria-based value tradeoff approach using the flexible and interactive tradeoff method.

The approach gives a different ranking of celebrities for two campaigns based on group members' perceived importance of the selection criteria in different scenarios. This group includes decision-makers (DMs) from the brand, marketing communication agency and brand's customers. Further, each group member has an almost equal say in the decision-making based on fuzzy evaluation and an interactive and flexible value tradeoff approach to celebrity selection for receiving a rank order.

The approach uses secondary data on celebrities and hypothetical scenarios. Comparison with other methods is difficult, as no other study proposes a multi-criteria group decision-making approach to celebrity selection especially in a social media context.

This approach can help DMs make more informed, objective and effective decisions on celebrity selection for their brands or campaigns. It recognizes that there are multiple stakeholders, including the end customers, each of whose views is objectively considered in the aspects of group decision-making through a fuzzy evaluation method. Further, this study provides a selection mechanism for a given context of endorsement by objectively and interactively encapsulating stakeholder preferences.

This robust and holistic approach to celebrity selection can help DMs objectively make consensual decisions with partial or complete information. This quantitative approach contributes to the literature on selection mechanisms of influencers, celebrities, social media opinion leaders etc. by providing a methodological aid that encompasses aspects of interactive group decision-making for a given context. Moreover, this method is useful to DMs and stakeholders in understanding and incorporating the effect of nature or context of the brand and the campaign type in the selection of a celebrity or an influencer.

The purpose of this paper is to highlight an innovative approach to explore and evaluate the sustainability perspectives in e-commerce channels for additive manufacturing (AM). This approach helps the stakeholders to perform strategic planning dependent on a scenario-based analysis.

The paper structures the problem of understanding the sustainability perspectives of e-commerce channels for AM using value-focused thinking to identify the related fundamental objectives. These objectives assist in creating dynamic scenarios based on fuzzy cognitive maps of different e-commerce channels for AM.

To evaluate the proposed research methodology, four scenarios were developed for each e-commerce channel for AM. The exploration and evaluation of one of these scenarios assisted in explaining the whereabouts of the process to aid in strategizing decision situations and understanding these channels better from the sustainability perspective.

The approach presented in this paper can be used by the practitioners to perform strategic planning for prioritizing sustainability in e-commerce channels for AM; considering the consequences and trade-offs wrt the other factors. Moreover, the scenario-based analysis can be performed depending on the problem requirements of the stakeholder.

The paper addresses the gap of understanding the theoretical aspect of the sustainability perspective in e-commerce channels for AM and the practical aspect of exploring and evaluating them. A scenario-based analysis for each e-commerce channel based on the fundamental objectives of sustainability provides insights for implementation and directions for future research.

Introduction: Healthcare facilities have witnessed deterioration, limited employee engagement, and communication gaps due to a lack of wireless technology. The Internet makes work and life quicker and more intelligent. The Internet of Things (IoT) is a scheme of interconnection equipped with unique identifiers in recent years. Artificial intelligence (AI) and IoT advancement allow employees to develop competent and predictive services and solutions in human resource (HR) practices. This chapter has been formulated to summarise and classify the existing research and better understand the past, present, and future of employee engagement by improving IoT interrelated devices in the healthcare industry.

Purpose: This study aims to categorise and overcome the challenges involved in HR practices. Effectively embracing IoT application-connected devices in the healthcare industry can enhance human resources management’s (HRM) role and measure performance assessment to improve employee engagement and productivity.

Methodology: In this study, the authors develop propositions dependent on a theory-based review. A systematic analysis was applied to minimise the challenges of HRM. The subject-related articles from different journal sources, like Scopus, Emerald, Web of Science, Springer, etc., were analysed based on engagement criteria. It was graphically recorded in a collective and informative way to emphasise the review outcomes. The study has presented the positive impacts of AI and IoT on engagement in health care.

Summary: This chapter accumulated theory-based knowledge about healthcare employee engagement and how IoT-based technology like AI can optimise employees’ engagement effectively. Further, it draws comparative benefits for a workforce to execute performance advancements and create future progressive aspects for healthcare employees.

The internet of things and just-in-time are the embryonic model of innovation for the state-of-the-art design of the service system. This paper aims to develop a fault-tolerant machining system with active and standby redundancy. The availability of the fault-tolerant redundant repairable system is a key concern in the successful deployment of the service system.

In this paper, the authors cogitate a fault-tolerant redundant repairable system of finite working units along with warm standby unit provisioning. Working unit and standby unit are susceptible to random failures, which interrupt the quality-of-service. The system is also prone to common cause failure, which tends its catastrophe. The instantaneous repair of failed unit guarantees the increase in the availability of the unit/system. The time-to-repair by the single service facility for the failed unit follows the arbitrary distribution. For increasing the practicability of the studied model, the authors have also incorporated real-time machining practices such as imperfect coverage of the failure of units, switching failure of standby unit, common cause failure, reboot delay, switch over delay, etc.

For deriving the explicit expression for steady-state probabilities of the system, the authors use a supplementary variable technique for which the only required input is the Laplace–Stieltjes transform (LST) of the repair time distribution.

For complex and multi-parameters distribution of repair time, derivation of performance measures is not possible. The authors prefer numerical simulation because of its importance in the application for real-time uses.

The stepwise recursive procedure, illustrative examples, and numerical results have been presented for the diverse category of repair time distribution: exponential (M), n-stage Erlang (Ern), deterministic (D), uniform (U(a,b)), n-stage generalized Erlang (GE_[n]) and hyperexponential (HE_[n]).

Concluding remarks and future scopes have also been included. The studied fault-tolerant redundant repairable system is suitable for reliability analysis of a computer system, communication system, manufacturing system, software reliability, service system, etc.

As per the survey in literature, no previous published paper is presented with so wide range of repair time distribution in the machine repair problem. This paper is valuable for system design for reliability analysis of the fault-tolerant redundant repairable.

The purpose of this paper is to study the performance metrics of redundant repairable machining system which is applicable in various systems like computer and communication system, manufacturing and production system, etc.

In the present investigation, the authors develop Markov model for the system consisting of identical active operating machines which are prone to breakdown. The operating machines are under the care of one permanent repair facility that provides time-sharing basis repair services. The maintenance is facilitated with the provision of standby machines of mixed type and permanent as well as additional repair facility. From the economic point of view, F-policy and N-policy to control the service and arrival of failed machines effectively are included.

For the performance analysis of the system in long run, the authors compute steady-state probabilities using product-type solution method recursively. Sensitivity analysis is performed numerically for various parameters by developing code in MATLAB.

The performance prediction done may be helpful for the system designers and decision makers for the improvement of the existing machining systems in various industries.

Markovian model for the performance prediction of fault tolerant multi-identical operating and standby machines redundant system is developed in generic frameworks by incorporating many noble features which were not all taken together by other researchers working on the same lines. The key concepts incorporated for the modeling of the concerned system is: F-policy, N-policy, time-sharing, and sensitivity analysis of availability and cost function.

Equipment reliability significantly impacts productivity, and in order to obtain high equipment reliability and productivity, maintenance and production decision should be made simultaneously to keep manufacturing system healthy. The purpose of this paper is to investigate the joint optimization of equipment maintenance and production decision for k-out-of-n system equipment with attenuation of product quality and to explore the impact of maintenance on the production and cost control for manufacturers.

A multi-period Markov chain model for k-out-of-n system equipment is set up based on the assumption that the deterioration of equipment is a pure birth process. Then, the maintenance cost, setup cost, inventory holding cost, shortage cost, production cost and the quality cost are analyzed with the uncertain demand and the attenuation of product quality stemmed from equipment deterioration. The total lowest cost per unit time and its specific calculation method are presented. Finally, the robustness and flexibility of the method are verified by a numerical example and the effects of equipment deterioration intensity and attenuation of product quality are analyzed.

The result shows that the joint decision model could not only satisfy the uncertain demand with low cost and strong robustness but also make the output products high quality level. In addition, the attenuation of product quality would influence the equipment maintenance and production decision and leads to the production waste and increases the operation cost greatly.

Implications derived from this study can help production maintenance managers and reliability engineers adequately select maintenance policy to improve the equipment efficiency and productivity with high quality level at a relatively low cost.

This research primarily aims to find and analyse the interaction among success factors for improving the performance of Indonesia’s dairy milk supply chain. Further, this research aims to formulate the right policies for improving the performance of the chain based on the success factor that belongs to cause groups.

The paper analyses 10 success factors for improving the performance of the Indonesian dairy supply chain with the decision-making trial and evaluation laboratory (DEMATEL) method and analyses the Delphi method to formulate the right policies for improving performance.

There are four important influencing factors that directly impact the overall system, i.e. the number of dairy cattle import, national milk demand, the total number of dairy farmers and the number of dairy cattle ownership or herd size. Several alternative policies have been designed by several experts according to the influencing factors, i.e. the government assists in the procurement of imported cattle, provides financial assistance to farmers in the form of low-interest financing, improves the partnership system between farmers and dairy cooperatives, provides a reward system for the farmers and increases the level of formality of contract between the farmers and cooperatives.

Interrelationships of each success factor and the most important influencing success factors could not be generally determined because it depends on the point of view of the experts. Future research can apply the success factors proposed by this research to the different dairy milk supply chain. Then, this research used only nine experts for formulating alternative policies. Future research may repeat this method using multiple experts to justify the validity of the research. Moreover, this research only explored 21 success factors of the increase in the performance of the Indonesian dairy supply chain. Future research should consider not only the supply side and number of dairy cattle but also several success factors from the causal relationship diagram in the broader dairy milk supply chain.

This research provides essential insights for policymakers, as they have to understand and evaluate the success factors before formulating several alternative policies.

The research has revealed that the right alternative policies can be designed, as the causal factor has been known.

This research contributes to applying a combination of causal relationship diagram of System Dynamic and DEMATEL method as a qualitative and quantitative method in one integrated way through performance dairy supply chain analysis. As a result, this research draws a policy for the dairy supply chain referring to the success factor as a cause for the low performance of the Indonesian dairy supply chain.

In this study, the authors investigate the effect of big data analytics capability (BDAC) on supply chain performance (SCP) to assess the mediating effect of supply chain innovation (SCI) and the moderating effect of a data-driven culture (DDC).

The authors collected the primary data through an online questionnaire survey from the manufacturing sector operating in Jordan. The authors used 420 samples for the final data analysis, which the authors performed via partial least squares structural equation modelling using SmartPLS 3.3.9 software.

The results indicate that BDAC has a strong relationship with SCI and SCP. SCI shows a positive relationship with SCP as well as a mediating effect on SCI. The authors confirmed that DDC moderated the relationship between SCI and SCP.

The authors developed a conceptual and empirical model to investigate the relationship between BDAC, SCI, DDC and SCP. The authors contributed new theoretical and managerial insights that add value to the supply chain management literature through testing the moderated-mediated model of these constructs in Jordan’s manufacturing sector.

The purpose of this paper is to examine the characteristics and the determinants of board structure in small- and medium-sized enterprises (SMEs) in the process of going public within the continental European corporate system.

These issues are explored through the study of all the initial public offerings (IPOs) in the Spanish equity market for growing SMEs, and the statistical methodologies of ordinary least squares regression and stepwise regression are applied.

The results show that board size is larger than the minimum level established in law and that boards are composed of a majority of non-executive directors. In addition, the determinants of firm characteristics of board structure are firm age, level of financial leverage, and ownership structure.

This research is significant since its findings should help entrepreneurs reflect on which board structure is most appropriate for this new stage of the life cycle of their company as a listed firm. This evidence is also of interest for regulators and investors, who can, therefore, better understand board structures of SMEs at the moment of IPO.

This paper is the first to study characteristics and determinants of the board of directors of growing SMEs at the moment of going public. This study implies a step forward in research into the governance of small business and IPO literature, since the results differ from the evidence found for large company IPOs and contribute towards the debate regarding the need to consider the context and the type of firm in corporate governance studies.

This case describes the events following an incident of a rape in a taxi associated with Uber, by its driver. Uber was an application based taxi operator. The events raised several issues for government systems and processes, such as need for regulation of new formats of business like application based taxi services, integrated databases, checks against forgery and holistic approach towards women safety. The case also brings out how an e-commerce business raises regulatory concerns.