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Traditional performance measurement models and frameworks fail to take into account the intricacies and specificity of service businesses. The important characteristics of services, role of employees and partners, important of measures and concurrent production and delivery need to be incorporated into the framework. This paper seeks to address these issues.

The research followed a case‐based methodology using semi‐structured interviews. Literature review and case‐based methodology led to the conception of initial deployment framework.

Existing scorecards do not emphasize the deployment aspects of the scorecard and overlook trade‐offs and benchmarking decisions.

The scorecard provides guidance for successful deployment. The framework incorporates the importance of service innovation and role of employees and partners into the scorecard. Relative decision trade off and benchmarking are an integral part of the deployment process.

The two founding blocks of the scorecard are value maximization theory proposition and Six Sigma methodology. The service scorecard supports stakeholders that drive business performance thus ensuring accountability, innovation and collaboration. The scorecard offers a set of measures that builds upon existing measures.

This paper considers a new class of problems arising in the distribution side of supply chains. It considers cooperative market expansion and price negotiation by wholesalers and manufacturers in a class of franchise‐type distribution markets. For wholesalers, expanding the coverage of geographically dispersed markets while retaining profitability of business requires new levels of price discounts from manufacturers. Manufacturers, on the other hand, attempt to limit such discounts to maintain a resale price to retailers. A successful negotiation results in a set of spatial markets with appropriate wholesale prices for each wholesaler. A sequential method is proposed for this purpose, assuming that the manufacturers and wholesalers are co‐operative and they exchange relevant information. The suggested process uses two optimisation models and is illustrated with a case study.

Applies the concept of buyer‐seller relationship to long‐term planning of wholesaling operations. Current practices indicate that this kind of relational exchange reduces overall costs more than the traditional market exchange. Aims to help to operationalize this partnership by selecting a stable set of manufacturers and warehouses for an entire planning period. Develops a multi‐period optimization model incorporating constraints necessary for such stable solutions. This model requires a large number of variables and constraints, and is computationally challenging for most practical‐sized problems. Therefore proposes heuristic method to decompose the model into subproblems by partitioning the planning period into blocks of years and then synthesize the subproblem solutions. Such decomposition may also be useful to solve large dynamic location planning problems.

Develops a decision‐making framework for the design and operation of a wholesaling system. Considers the new roles of wholesaling and proposes an optimization model to satisfy best the service requirements at the minimum cost. Using this model, a prototype decision‐support system is developed to build various scenarios and suitable models for these scenarios. A number of management reports are prepared to help managers negotiate prices or volumes, consolidate warehouses, select transport modes, and test the effectiveness of proposed service packages.

With gaining popularity, online communities are increasing. It is leading to the data and information overflow. So, there are some challenges like cyber frauds, cyberbullying, etc. while engaging with online communities. Not only this, anonymity of the participants, stress and racism are also big challenges in online communities' interaction. Online harassers' attack tactics have changed over time. In addition, there are challenges like quality of discussion, inequality in participation of the users, etc. may scale online communities towards incitement and activism. Therefore, this study will try to analyse these challenges for overall benefit of the society.

The underlying fuzzy set theory is employed to handle the fuzziness of users' perceptions since the attributes are expressed in linguistic preferences. Through exhaustive literature review, the authors have identified 15 challenges. These challenges are further categorised as cause and effect by using DEMATEL (Decision-Making Trial and Evaluation Laboratory) approach.

Lack of strategic planning and uninspired discussions between users has emerged as a major challenge in cause category. This study further demonstrates how individual challenge can be managed and developed to navigate the online communities to maintain a healthy environment in society.

Results are based on limited dataset. Therefore, findings cannot be generalised for all online communities.

The research findings offer a suitable direction to policymakers to formulate and design policies, laws and regulations to increase user engagement in the online community. The study is beneficial to firms and researchers in understanding the factors influencing effective management of online communities.

Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.

Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.

Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.

Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.

Introduction: Artificial intelligence (AI) is being extensively used to solve complex problems in the industry. AI provides several benefits such as providing visibility in the processes, reducing time, improving accuracy, saving time, helping in the decision-making process, etc. Due to the range of benefits of AI technologies, organisations readily adopt this technology. However, there are several challenges that the organisation faces during the implementation of AI. These challenges are in context to human resource (HR) development for successful implementation of AI across different functions and are discussed in this chapter.

Purpose: Although we know that AI technology is widely accepted in human resource management (HRM) due to its various benefits. But the organisations face many challenges during the implementation of AI. The focus of the study is to explore the literature on AI in HRM, identify the challenges of implementing AI and provide potential future research direction based on a systematic literature review.

Methodology: To explore the literature on AI in HRM, the study undertakes a systematic literature review. The study identifies, analyse and classifies the literature to provide a holistic view of HR challenges in implementing AI. The study is built on a review of 47 documents, including the articles, book chapters and conference papers using the Scopus database for the past 10 years (2012–27 January 2022).

Findings: The study provides an overview of the documents published in Scopus in this area through a systematic literature review. The study reveals that a significant amount of growth in the publication has been shown in the past 10 years. The maximum and continuous growth is shown after 2017. The maximum number of papers are published in India, the USA and China. The study identifies major eight challenges of AI implementation in HRM. The study also provides a secondary case to deep dive in this area based on a systematic literature review.

Research Limitation/Implication: The challenges identified in the study are not empirically tested. Each of the identified challenges should be empirically examined. This study has expanded the body of knowledge of AI in HRM. This study will help the academicians and practitioners work on the identified challenges and help the organisations ease in adopting AI.

Originality/Value: This study represents the first work that integrates AI implementation challenges in HRM.

This paper aims to show the pragmatic studies that examine whether novel COVID-19 affects the national and international stock markets and reinforces the existing literature by highlighting the factors that are resultant from COVID 19.

The systematic literature review and bibliometric approach have been used in the study covering 585 selected articles published in journals of high repute from January 2020 to January 2022. The process of bibliometric analysis has been divided into three stages, namely, assembling, arranging and assessing. From the Scopus database, one of the most reliable and authentic database total of 585 records were collected, out of which 12 were specifically focused on communities, and information gathered in the comma-separated value documents design was compared and interpreted based on year, document types, subject area, country and research fields with the help of graphs and pie charts. The study has analyzed fact-based and reliable studies to draw inferences from existing literature regarding the pandemic impacting the financial markets. In the extant study, an attempt has been made to explore the factors that are resultant from the COVID-19 pandemic and affects the stock market performance, which can be further classified into a few common factors by using factor analysis.

It originated from the majority of the studies that the stock market retorted destructively to the upsurge in the figure of COVID-19 cases and fatalities. It also emphasized that the market has reacted differently in comparison to earlier catastrophes such as the great depression of 2008 and the Spanish flu. Various factors such as fear of losing capital, standstill economy, lower valuation, increased mortality rate, halt in business operations, retrenchment, trade war, liquidity issues, panic buying and selling, digitalization, negative media coverage, government interference, financial behavior of investors, hoarding of COVID supplies, promotion of start-up in health-care and education sector, news bulletins, prevention campaigns, use of medical devices and COVID-19 vaccination, etc. have been conferred from the studies that have an immediate consequence on the actions of investors in the stock market. It was further highlighted in the study that the Indian stock market has been less explored in respect of implications of COVID-19 contagion as the majority of studies were based on either international stock exchanges or combinations of varied nation’s stock markets. It was witnessed in the interpretation section that the number of studies is increasing at a fast pace as new variants of COVID-19 are emerging over time. Significant contribution has been done in enhancing the literature on COVID-19 and the stock market by China and the USA. The maximum contribution in this domain has been done in the form of articles in the present literature. Few studies were focusing on communities, so the present study will try to fill this research gap to some extent.

This conceptual paper is demarcated by unsatisfactory analyses of writings from multi-discipline to get a comprehensive scope of notional understanding. Furthermore, there is a perchance that some other imperative phenomena or variables that prejudiced trading bustle have not been captured by present reviews of research papers. The influences of other macroeconomic variables should be explored to understand the concrete results of this pandemic.

Most of the studies were based on foreign stock exchanges, so there is an opportunity to explore the Indian stock market concerning the implications of the coronavirus pandemic. In the literature, it was examined that short-term studies have been undertaken, which cannot determine the long-term implications of COVID-19. Over time, besides COVID-19, various other factors have started impacting the stock market, so it has become difficult to examine the influence of COVID-19 on the stock market in isolation.

The study will be helpful for future learnings in the arena of the stock market as it provides vast exposure to the present literature related to the impact of COVID-19 on economic markets. On the other hand, investors will also become aware of factors that are resultant of COVID-19 and will take the right decisions to save their investments in light of pandemic implications. The extensive review of studies will also help enterprising communities to take judicial steps to remain active in the period of economic slowdown.

The paper provides significant implications to the investors in the stock market, and it will provide useful insight to improve their returns on their portfolios. The learning from the study will help investors to take fruitful decisions considering the uncertainty during the pandemic period. The inferences drawn from rich existing literature will be guiding enterprises to take timely actions to avoid the situation of loss in the market and adapt new models to ensure continuity of business operations. Different markets had reacted differently, so investors need to be cautious before taking trading decisions.