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Data, either in primary or secondary form, represent the core strength of quantitative research. However, there is significant difference between collected data and the final researchable data. The data collection is driven by objectives of the research. The data also could be in various formats at different sources. The collected data in its original form may contain systematic and random errors. Such errors need to be cleaned from the data which is termed as data cleaning process.

The present chapter discusses about the different methodologies and steps that may be helpful for fine tuning the data into researchable format. The discussions are instantiated with the applications of methodologies on a set of financial data of companies listed in Bombay Stock Exchange. Various steps involved in transformation of collected data to researchable data are presented. A schematic model including data collection, data cleaning, working with variables, outlier treatment, testing the assumption of statistical test, normality, and heteroscedasticity is presented for the benefit of research scholars. Beyond this generic model, this paper focuses exclusively on financial data of listed companies in the Bombay Stock Exchange. The challenges involved in various sources, data gathering and other pre-analysis stages are also considered. This is also applicable for research based on secondary data sources in other fields as well.

Currently, digital technology has been proposed as a new archetype for developing an effective traceability system in the perishable food supply chain (FSC). Implementation of such a system needs significant investment and the burden lies with the members of the supply chain. The purpose of this paper is to examine the impact on the profit of the supply chain members due to the implementation of an effective traceability system with such a large investment. The study also tries to explore the impact of the implementation of such a system by coordination among the members through a cost-sharing mechanism.

A two-level supply chain that comprises a supplier and retailer is analyzed using a game-theoretic approach. The mathematical models are developed considering the scenario for an individual, centralized and both members invest using a cost-sharing mechanism. For each of the models, the impact of product selling price, information sensing price and quality improvement level on profit is analyzed through numerical analysis.

The study reveals that consumer involvement can be a strong motivation for the supply chain members to initiate investment in the traceability system. Further, from an investment perspective cost-sharing model is beneficial compared to the individual investment-bearing model. This mechanism can coordinate as well as benefit the FSC members. However, the model is less beneficial to the centralized model from profit and quality improvement levels.

Food wastage can be less from supplier and retailer perspectives. Moreover, consumers can purchase food items only after verifying their shipping conditions. Consequently the food safety scandals can be reduced remarkably.

Digital technology adoption in the perishable FSC is still considered emerging. The present study helps organizations to implement a traceability system in the perishable FSC through consumer involvement and a cost-sharing mechanism.

This paper aims to incorporate a hybridized advanced sine-cosine algorithm (ASCA) and advanced ant colony optimization (AACO) technique for optimal path search with control over multiple mobile robots in static and dynamic unknown environments.

The controller for ASCA and AACO is designed and implemented through MATLAB simulation coupled with real-time experiments in various environments. Whenever the sensors detect obstacles, ASCA is applied to find their global best positions within the sensing range, following which AACO is activated to choose the next stand-point. This is how the robot travels to the specified target point.

Navigational analysis is carried out by implementing the technique developed here using single and multiple mobile robots. Its efficiency is authenticated through the comparison between simulation and experimental results. Further, the proposed technique is found to be more efficient when compared with existing methodologies. Significant improvements of about 10.21 per cent in path length are achieved along with better control over these.

Systematic presentation of the proposed technique attracts a wide readership among researchers where AI technique is the application criteria.

The purpose of this paper is to identify and model critical barriers to cloud computing adoption (CCA) in Indian MSMEs by the interpretive structural modeling (ISM) approach.

In this paper, through a literature survey and expert opinions, 14 critical barriers were identified, and the ISM tool was used to establish interrelationship among the identified barriers and to determine the key barriers having high driving power.

After analyzing the barriers, it was found that three barriers, namely, lack of confidentiality (B8), lack of top management support (B3) and lack of sharing and collaboration (B2) were most significant.

The developed model is based on the expert opinions, which may be biased, influencing the final output of the structural model. The research implications of the developed model are to help managers of the organization in the understanding significance of the barriers and to prioritize or eliminate the same for the effective CCA.

This study is for the first time an attempt that has been made to apply the ISM methodology to explore the interdependencies among the critical barriers for Indian MSMEs. This paper will guide the managers at various levels of an organization for effective implementation of the cloud computing practices.

The purpose of this paper examines the relationship between dimensions of workplace spirituality (meaningful work, sense of community and alignment with organizational values) and employee loyalty (intention to stay, benefit insensitivity toward alternate employers and word of mouth about the organization), in the context of millennials who are three times more likely to change jobs, than other generations.

This descriptive study was conducted among a sample of 308 employees, working in private sector organizations in India. Self-reporting questionnaires were administered among the respondents, who were selected through a purposive sampling method and structural equation modeling was done to test the hypotheses.

The three dimensions of workplace spirituality had varying influences on the outcomes variables. Alignment with organizational values was positively related to all dimensions of employee loyalty, whereas the sense of community had a positive association with intention to stay and benefit insensitivity while meaningful work indicated positive influence only on benefit insensitivity. The findings, in general, suggest that employees’ experience of workplace spirituality has significant positive influence on their loyalty toward the organization.

This study is pioneering in conceptualizing and testing a theoretical model linking workplace spirituality and employee loyalty, particularly in the context of millennials, who form 50 per cent of the workforce and reportedly exhibit higher turnover intentions. The study gains relevance in the context of reports about monetary/non-monetary preferences among millennial employees and that the generation is not too keen in working with teams, but would rather prefer working in an organization, which provides space for self-actualization in individual growth. Implications for their experience of workplace spirituality and outcomes are elaborated, thus striving to fill a gap in the existing literature.

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.

The purpose of this paper is to examine the influence of workplace spirituality on employee well-being and intention to stay with the organization among teachers in technical higher education institutions. Furthermore, the study endeavors to test the difference in model estimates across two groups of teachers who differ in their intentional career choice.

This descriptive study was conducted amongst a sample of 523 teachers working in technical educational institutions in India. Self-reporting questionnaires were administered among the respondents, who were selected through purposive sampling method. Structural equation modeling and multi-group analysis were done to test the hypotheses.

Analysis revealed that workplace spirituality enhanced employee well-being and intention to stay. Differences were observed on the effects of different dimensions of workplace spirituality on job outcomes. Also, teachers’ intentional career choice was found to moderate these relationships.

This study is pioneering in conceptualizing and testing a theoretical model linking workplace spirituality, employee well-being and intention to stay, particularly in the context of teachers who differ in their intentional career choice. Implications with regard to the experience of workplace spirituality and job outcomes in the specific context of teaching are elaborated, thus striving to fill a gap in existing literature.