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The purpose of this paper is to identify the most significant barriers to successful implementation of information technology (IT) in higher educational institutions (HEIs) of India. Although, educational institutions are investing in IT, they have been not been able to leverage it the same way as other business organizations. The present investigation will assist the management of HEIs to distinguish the key barriers affecting productive IT implementations and further take appropriate measures to deal with it.

For the purpose of the study, focus group and semi-structured interviews were conducted with academicians, administrators, functional heads, and IT staff from various HEIs of India. This research attempts to discover the major barriers to successful implementation of IT in HEIs using an interpretive structural modeling (ISM) methodology. Furthermore, structural analysis and classification of barriers is done using MICMAC analysis.

The results identified the key barriers that if dealt with can help overcome or lower the effect of other barriers preventing successful IT implementation in HEIs. It will provide roadmap to managers and administrators of HEIs to take appropriate measures to overcome the major barrier to effective implementation of IT.

Several authors have been studied barriers to implementation of IT in industry and educational institutions, but none have found the most significant barriers that affect successful implementation of IT and may drive other impediments. This research draws inspiration and is being carried out for Indian HEIs.

The present study aimed to evaluate the psychometric properties of the short form of personal optimism and self-efficacy optimism-extended (POSO-E) among Indian teachers.

Two studies were conducted to adjudge the reliability and validity of the scale. In the first study, the sample of 510 respondents was randomly divided into subsamples. The first subsample was subjected to the Exploratory Factor Analysis which yielded a two-factor solution explaining 71.02% of the variance. This model was subjected to the Confirmatory Factor Analysis using a second subsample. Acceptable model fit indices suggested factorial validity of the two-dimensional POSO-E among Indian teachers. In the second study, acceptable Cronbach's alpha and composite reliability estimates (greater than 0.70) indicated the scale's reliability. Also, as expected, personal optimism, self-efficacy optimism and overall optimism reported a positive correlation with spiritual well-being and a negative association with distress. It confirmed the criterion validity of the POSO-E among Indian teachers.

The results showed appreciable psychometric properties of the POSO-E in the context of Indian teachers. The study offered a valid and reliable scale to measure teachers' optimism levels. It is poised to generate renewed interest among scholars to emphasize teachers' positive and optimist thinking. The findings also reported a positive association between teachers' optimism and spiritual well-being. It suggests that spiritual practices and interventions could be used to develop an optimistic academic workforce.

The study is one of the pioneer studies that evaluated the reliability and validity of the POSO-E among Indian teachers.

The governments of different nations implemented various policy measures in response to the COVID-19 outbreak. These policy measures had a negative impact towards freight transportation. Further, the shock and ripple effect on the other supply chain complicates the recovery process of freight transportation. The transformation of freight transportation in the post-COVID-19 world was reported to be unsustainable. Thus, emerged the requirement to formulate the recovery measures in the context of freight transportation. This study aims to identify and model the recovery measures for sustainable freight transportation (SFT).

In this study, 13 critical recovery measures are established from the literature and finalized with the help of an expert panel. An integrated Grey Decision-Making trial and evaluation laboratory is used to prioritize and establish the cause-effect relationships among the identified critical recovery measures. Further, sensitivity analysis is performed to ensure the robustness of the proposed model.

The present study reflects that Efficient traffic management (M13), sustainability focused policies (M2), sensitization of stakeholders (M10), financial support (M9) and adoption of 4 R practices (M12) are the top five critical recovery measures for SFT. The results highlight that the transport sector needs to retain the learning from the COVID-19 period to operate under low workforce availability. Further, the emerging economies are suggested to promote local manufacturing to reduce the lead time and risk of unavailability. The study findings reflect that attaining sustainability without considering the social dimension of sustainability is impossible. Also, the results shed light on the controllable and uncontrollable recovery measures.

The findings from the study would assist policymakers and practitioners in re-formulating the recovery measures for freight transportation considering the aspect of sustainability.

The financial technology (FinTech) era has brought a revolutionary change in the financial sector’s customer experiences at the national and global levels. The importance of artificial intelligence (AI) in the context of FinTech services for enriching customer experiences has become a new norm in this modern era of technological advancement. So, it becomes crucial to understand the customer’s perspective. The current research ranks the factors and sub-factors influencing customers’ perceptions of AI-based FinTech services.

The sample size for this study was decided to be 970 respondents from four Indian cities: Mumbai, Delhi, Kolkata and Chennai. The Fuzzy-AHP technique was used to identify the primary factors and sub-factors influencing customers’ experiences with AI-enabled finance services. The factors considered in the study were service quality, trust commitment, personalization, perceived convenience, relationship commitment, perceived sacrifice, subjective norms, perceived usefulness, attitude and vulnerability. The current research is both empirical and descriptive.

The study’s three top factors are service quality, perceived usefulness and perceived convenience, all of which have a significant impact on customers’ experience with AI-enabled FinTech services discussing sub-criteria three primary criteria for customers’ experience for FinTech services include: “Using FinTech would increase my effectiveness in managing a portfolio (A2)”, “My peer groups and friends have an impact on using FinTech services (SN3)” and “Using FinTech would increase my efficacy in administering portfolio (PU2)”.

The current study is limited to four Indian cities, with 10 factors to understand customers’ preferences in FinTech. Further research can focus on other dimensions like perceived ease of use, familiarity, etc. Future studies can have a broader view of different geographical locations and consider new tech to understand customer perceptions better.

The study’s findings will significantly assist businesses in determining the primary aspects influencing customers’ experiences with AI-enabled financial services. As a result, they will develop strategies and policies to entice clients to use AI-powered FinTech services.

Existing AI research investigated several vital topics in the context of FinTech services. On the other hand, the current study ranked the criteria in understanding customer experiences. The research will substantially assist marketers, business houses, academicians and practitioners in understanding essential facets influencing customer experience and contribute significantly to the literature.

The purpose of this paper is to understand how logistics service providers manage their operations for sustainable service quality and operational excellence. This study is also trying to propose a framework for measuring service quality index along with some research propositions for analyzing service quality and operational excellence.

The detailed case analysis on sustainable practices of Indian logistics service providers (LSPs) has been done on the basis of case study approach suggested by Eisenhardt (1989). After this case analysis, service quality index is evaluated for the given company based on Cleveland et al. (1989) framework. The SWOT and performance analysis of the case organization has been also done, which is further followed by development of framework and propositions for analyzing service quality and operational excellence for LSPs.

It can be observed from in-depth case analysis that LSPs are making use of green practices to conserve resources for long-term sustainability. LSPs optimize their resources and use sustainable practices in the form of use of reusable and recyclable packaging materials, rain water harvesting, use of solar panels, innovations such as trucking cube, safety concern toward goods and contribution toward environment and society.

Resources optimization and green management practices are essential for LSPs for benchmarking environmental performance and for improving service quality and operational excellence.

This study has tried to analyze green logistics practices for sustainable service quality and proposed a framework for quantifying sustainability.

The purpose of this study is to analyze sustainability issues in the context of logistics service providers (LSPs) and to evaluate the sustainability index of an LSP in the Indian scenario.

The case study method is used to understand the sustainability issues of an Indian LSP. The situation–actor–process (SAP) and learning–action–performance (LAP) framework has been applied to critically examine the sustainability issues of the case organization. The information about the case study is collected from both primary and secondary sources, and a detailed analysis of different sustainability issues in the context of an Indian case has been done. The sustainability index for the case organization has been also evaluated.

It can be observed from the in-depth case analysis that LSPs have started making use of green practices for long-term sustainability, but adoption is very slow in developing countries such as India. Based on SAP-LAP analysis of an Indian LSP, suitable actions are recommended for improving the performance of LSPs.

Adoption of sustainable practices by LSPs can lead to sustainable performance. Sustainability index can assist LSPs to benchmark their sustainability performance to meet the market requirements.

The novelty of this study is to analyze the issues related to implementation of sustainable practices in an Indian LSP through SAP-LAP framework. It also illustrates the evaluation of sustainability index.

The purpose of this study is to identify sustainability practices and to propose a framework for evaluating the sustainability index of logistics service providers (LSPs).

Sustainable practices followed by LSPs are identified through literature review and analysis of a case study. Thirteen such sustainable practices are identified. Thereafter, with expert inputs, nine sustainable practices are shortlisted and considered for the evaluation of the sustainability index in the proposed framework. Graph Theory Matrix Approach has been applied to evaluate the sustainability index of an LSP.

Major practices identified for evaluating sustainability index include the use of recyclable packaging, use of renewable energy sources, green procurement, reduction in carbon emissions, use of CNG/electric vehicles, rainwater harvesting and so on. The sustainability index of an LSP is evaluated by using the proposed framework.

LSPs can benchmark their sustainability index with respect to the best in the industry. Based on it, LSPs can also identify potential areas for improvement.

Novelty of the study lies in the proposed framework for evaluation of sustainability index which can be used to develop strategies for green logistics. LSPs can also improve their performance in terms of sustainability measures by adopting green logistics.

The purpose of this study is to identify factors that are important for logistics organizations from the perspective of manpower readiness for digitization of logistics operations. The study also prioritizes the identified factors and also evaluates the readiness index of manpower for the digitalization of logistics processes.

The factors for manpower readiness are identified through literature review and analysis of a case study. Three major categories of factors are identified. These are organizational, behavioural and technological factors. Under these three major categories of factors, 18 sub-factors are identified. Thereafter, with experts' inputs, the factors are prioritized using Fuzzy analytic hierarchy process (AHP). Further, a case illustration of an Indian logistics company has been taken to understand the current processes, technical capabilities, manpower skills and organization culture. After the case analysis and expert inputs, the manpower readiness index has been evaluated by using graph theory matrix approach (GTMA).

The prioritization of manpower readiness factors has been done using Fuzzy AHP. Organizational factors are found to be the most important factors which require quick attention. Sub-factors that are most important for building competencies in the logistics sector are providing the right training on functional skill development (0.129), top management support and commitment for digitalization (0.117), and organizational culture for process digitalization (0.114), etc. Finally, framework for evaluation of manpower readiness index for logistics operations in the digital age has been illustrated for a case company.

Indian logistics companies can benchmark their readiness index with respect to the best in the industry. Based on the readiness index, logistics companies can analyse their position, gaps from best and worst and can also identify potential areas for improvement.

The novelty of the study lies in the development of a framework for manpower readiness for digitalization in the logistics sector. In literature, this field is very less researched and provides the scope for developing strategies for improving manpower competencies for Industry 4.0. Logistics companies can improve their performance by making their manpower ready based on results obtained for readiness index.

Knowledge management (KM) is an important topic in the age of big data, and this study adds to the existing body of literature by providing a novel KM perspective on the technological phenomenon of artificial intelligence (AI). This study aims to discover how AI might facilitate knowledge-based business-to-business (B2B) marketing. In this chapter, the authors take a close look at the building blocks of AI and the relationships between them. Future research directions and also the effects of the various market information building components on B2B marketing are discussed. The study’s approach is theoretical; it tries to provide a framework for characterising the phenomenon of AI and its constituent parts. Additionally, this chapter provides a methodical analysis of the three categories of market information crucial to B2B marketing: knowledge of customers, knowledge of users, and knowledge of external markets. This research looks at AI through the lens of the conventional data processing framework, analysing the six pillars upon which AI systems are founded. It also explained how the framework’s components work together to transform data into actionable information. In this chapter, the authors will look at how AI works and how it can benefit B2B knowledge-based marketing. It’s not aimed at AI experts but rather at general marketing managers. In this chapter, the possible effects of AI on B2B marketing are discussed using examples from the real world.