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The purpose of this paper is twofold. First, in order to understand mobile customer relationship management (mCRM) and data mining application in the mCRM, this paper aims to present a conceptualization of mCRM in respect of data mining. Second, the paper also aims to develop the empirically grounded framework of the mCRM from data mining perspective.

The empirical paper is used to gain a conceptual view of mCRM. Semi‐structured interviews and contact methodology is used to form the main data source through which the major concerns and issues of mCRM are identified. This lead to holistic framework of mCRM. The paper followed the paradigm of natural science research on information technology by March and Smith and Hervner et al.

The framework identified three critical issues that are categorized as customer care information center, data store and data access systems, and mobile services and technology. The paper on various existing literatures in mCRM strategies and data mining leads to the development of the mCRM framework. The applications of methodology in data mining helped in identifying and exploring mCRM processes. The data mining based framework identifies issues related to customer attrition, customer life time value analysis and customer churn analysis while moving towards mCRM.

The suggested framework would serve as a guideline to all mCRM product vendors and will be considered as a structured consistent procedure for applying mCRM using data mining tools and techniques. The paper explored various studies in the area of mCRM and data mining and shed light on emerging issues in mCRM area. The suggested framework would give an organization, product developers, and management thinker's valuable insights on application of data mining tools and techniques in mCRM application.

The purpose of the paper is to provide a thorough analysis of the concepts of business intelligence (BI), knowledge management (KM) and analytical CRM (aCRM) and to establish a framework for integrating all the three to each other. The paper also seeks to establish a KM and aCRM based framework using data mining (DM) techniques, which helps in the enterprise decision‐making. The objective is to share how KM and aCRM can be integrated into this seamless analytics framework to sustain excellence in decision making using effective data mining techniques and to explore how working on such aCRM system can be effective for enabling organizations delivering complete solutions.

This paper is based on focused and dedicated study of the literature present on the aCRM, KM and data mining techniques. The paper considered how to develop a strategy and operational framework that would build aCRM on the foundation of existing DM techniques and KM approach to meet the business challenges. Based on this research, a customized, integrated framework, to match the needs of business was designed.

KM focuses on managing knowledge within the organization and aCRM focuses on gaining analytical information from the customer data. Both KM and aCRM help in the decision making process and understanding. This knowledge is difficult to uncover. Hence, this paper explains the importance of data mining tools and techniques to uncover knowledge by the integration between KM and aCRM. This paper presents an integrated KM and aCRM based framework using DM techniques.

All the firms may not be in favor of adopting KM while implementing aCRM. The KM requires a convalesce of organizational culture, technology innovations, effective work force in culminating knowledge dissemination in all business domains.

The organizations implementing this knowledge enabled aCRM framework would be easily able to convert their business knowledge via the analytical CRM to solve many business issues, such as increase response rates from direct mail, telephone, e‐mail, and internet delivered marketing campaigns, increased sales and increased services. With aCRM, firms can identify their most profitable customers and use this knowledge for promotional schemes for those customers as well as identify future customers with prediction on ROI.

The need for the integration of KM and aCRM is clear. It is written for practitioners who are looking for approaches to improve business performance and maintain high profits for their business by incorporating knowledge‐enabled aCRM in their setup.

The purpose of this paper is to propose the important principles for successful CRM analytics (aCRM) in organizations.

The paper employs the approach of identifying the major critical business information needed for aCRM in organizations. The approach to identify the critical business is based on the cross‐pollination of information related to aCRM. Analyzing aCRM from the hybrid blends of business sources provides a clear understanding in a more realist dimension.

The paper finds that knowing what to capture is fundamental to business alignment of aCRM in a typical business environment.

The successful implementation of the principles of aCRM will help organizations to measure the effectiveness of their direct marketing activities.

The chosen research strategy was to survey aCRM in organizations that are being incorporated worldwide and analyze their content, looking for similarities and complementarities in their nature of business strategies. First, the paper identified existing aCRM systems, examining available listings of aCRM systems in organizations, and expanding them through Internet searches. The paper then collected detailed information on aCRM systems, and examined the descriptions, nature of organizations, their business strategies, their view on business values etc.

Sustainable development goals (SDGs) are gaining significant importance in the current environment. Many businesses are keen to adopt SDGs to get a competitive edge. There are certain challenges in realigning the present working scenario for sustainable development, which is a primary concern for society. Various firms are adopting sustainable engineering (SE) practices to tackle such issues. Artificial intelligence (AI) is an emerging technology that can help the ineffective adoption of sustainable practices in an uncertain environment. In this regard, there is a need to review the current research practices in the field of SE in AI. The purpose of the present study is to comprehensive review the research trend in the field of SE in AI.

This work presents a review of AI applications in SE for decision-making in an uncertain environment. SCOPUS database was considered for shortlisting the articles. Specific keywords on AI, SE and decision-making were given, and a total of 127 articles were shortlisted after implying inclusion and exclusion criteria.

Bibliometric study and network analyses were performed to analyse the current research trends and to see the research collaboration between researchers and countries. Emerging research themes were identified by using structural topic modelling (STM) and were discussed further.

Research propositions corresponding to each research theme were presented for future research directions. Finally, the implications of the study were discussed.

This work presents a systematic review of articles in the field of AI applications in SE with the help of bibliometric study, network analyses and STM.

The purpose of the chapter is to integrate the understanding of diversity from different perspectives in Indian context and see how the holistic view emerges.

The methodology used is primarily the literature review of the concepts and their evolution in Indian context and the use of secondary sources to extract praxis information.

It emerged from the exploration on diversity practices at the societal as well as organizational level in India that the country demonstrates intent to mainstream the people from different wakes, but with the changing context the format of the practices has changed.

The basic premise of the chapter needs to be explored further through primary data from practice.

This chapter is novel in a way that it integrates the diversity scholarship of four different streams viz. caste, gender, disability, and generation. Most of the existing research focuses only on a thin slice/one key dimension of diversity.

This chapter discussed the role of business intelligence (BI) in healthcare twofold strategic decision making of the organization and the stakeholders. The visualization techniques of data mining are applied for the early and correct diagnosis of the disease, patient’s satisfaction quotient and also helpful for the hospital to know their best commanders.

In this chapter, the usefulness of BI is shown at two levels: at doctor level and at hospital level. As a case study, a hospital is taken which deals with three different kinds of diseases: Breast Cancer, Diabetes, and Liver disorder. BI can be applied for taking better strategic decisions in the context of hospital and its department’s growth. At the doctor level, on the basis of various symptoms of the disease, the doctor can advise the suitable treatment to the patients. At the hospital level, the best department among all can be identified. Also, a patient’s type of admission, continued their treatments with the hospital, patient’s satisfaction quotient, etc., can be calculated. The authors have used different methods like Correlation matrix, decision tree, mosaic plots, etc., to conduct this analysis.

Building on the arguments of expectancy theory and social exchange theory, the present study provides insights into the process by which pay-for-performance (PFP) impacts employee job performance.

Based on a sample size of 226 employees working in a technology company in India, the study examines the relationships between PFP, procedural justice, organizational citizenship behavior (OCB) and employee job performance. Data on perceptions of PFP and procedural justice were collected from the employees, data on OCB were collected from the supervisors and the data on employee job performance were collected from organizational appraisal records.

The study found support for the positive relationship between PFP and job performance and for the sequential mediation of the relationship between PFP and job performance via procedural justice and OCB. Further, procedural justice was found to mediate the relationship between PFP and OCB.

The study was cross-sectional, so inferences about causality are limited.

The study tests the relationship between PFP and employee job performance in the Indian work context. The study shows that the existence of PFP is positively related to procedural justice which, in turn, is positively related to OCB. The study found support for the sequential mediation of PFP-job performance relationship via procedural justice and OCB.

The study provides an insight into the underlying process through which PFP is related to employee job performance. To the best of our knowledge, such a study is the first of its kind undertaken in an organizational context.

The presence of barriers in the supply chain finance (SCF) of small and medium enterprises (SMEs) cripples the productivity and efficiency of SMEs and makes it challenging to execute strategies. SCF barriers can be internal and external which tend to impede the desired performance and profitability of the SMEs. Therefore, the purpose of this paper is to discern the possible SCF barriers and analyze the criticality of the barriers to understand how they impact on the SMEs market of India.

This study proposes a novel hybrid approach called best worst method (BWM) to evaluate the discerned barriers. BWM technique is espoused to appraise the SCF barriers, so that the decision-makers can rationally comprehend the reason behind dominance of one barrier over other. Although such an assessment may possibly vary for different industries, that is why proposed approach is generic in nature and can be applied in real-world cases. The robustness of the suggested model is also assessed through sensitivity analysis.

SCF barriers are identified through extensive literature review and inputs from the industry. The results derived through BWM approach concludes that the “Financial Barriers” are censorious and foremost inhibitors for SMEs to flourish, therefore, require special attention by the top management. Likewise, “Supply and Suppliers Barriers” are ranked second, conversely, “Market and Policy Related Barriers” are found least critical in nature in SMEs of India.

This work is specific to SCF barriers and other barriers have not been touched upon. The study is based on expert panel opinion for seeking information which is restricted to Indian context, as the members of the expert panel belong to same geography.

This research could aid decision-makers and strategists to comprehend the deep-rooted initiatives to achieve a comprehensive implication of SCF across SC network. By assessing SCF barriers, this study helps SMEs to understand their shortfalls and in answering the pertinent question of how to gain excellence in this intensely competitive market.

SMEs are considered as engines of economic development worldwide. India too is striving for increasing the growth and development of SMEs in every aspect, to gain operational excellence, to make profits or employment generation but presence of SCF barriers makes it difficult to achieve this in Indian SMEs. Therefore, it is imperative to analyze the criticality of the SCF barriers to understand how they impact on SMEs market of India. The paper illustrates the modeling of SCF barriers among SMEs using BWM approach, exhibiting how comprehending barriers can improvise productivity and efficiency of the SCs in SMEs.