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Tourism industry has extensively embraced technologies to enhance operational competencies, service quality over and above customer satisfaction. This chapter presents Information Technologies as an innovative approach to promote tourism destination from Destination Management Organization’s (DMO’s) perspective. Precisely, attention has been given on how destination managers are applying different types of technological tools to promote their tourism business. In recent times, DMOs are implementing several promotional tools with the use of technology like embracing the mobile technology, Social Media, online ads, and building effective marketing strategies innovatively to compete with the cut-throat competition in travel trade. It also tried to provide an insight understanding of the changes in destination manager’s promotional strategies with the advancement of technologies. Based on primary research with destination managers and using the literature review, a collection of technology-based tools used by DMOs for the promotion of tourism at the destination level was identified and their individual impacts were measured to identify the effects on the profit generation. To accomplish the mentioned objectives, an explorative study based on basic demographic analysis along with Pearson correlation and Simple Linear Regression was conducted to examine the individual impacts of the different promotional tools used by DMOs.

The aim of this paper is to study the effect of deterministic roughness and small elastic deformation of surface on flow rates, load capacity and coefficient of friction in Rayleigh step bearing under thin film lubrication.

Reynolds equation, pressure-density relationship, pressure-viscosity relationship and film thickness equation are discretized using finite difference method. Progressive mesh densification (PMD) method is applied to solve the related equations iteratively.

The nature and shape of roughness play a significant role in pressure generation. It has been observed that square roughness dominates the pressure generation for all values of minimum film thickness. Deformation more than 100 nm in bounding surfaces influences the film formation and pressure distribution greatly. Divergent shapes of film thickness in step zone causes a delay of pressure growth and reduces the load capacity with decreasing film thickness. The optimum value of film thickness ratio and step ratios have been found out for the maximum load capacity and minimum coefficient of friction, which are notably influenced by elastic deformation of the surface.

It is expected that these findings will help in analysing the performance parameters of a Rayleigh step bearing under thin film lubrication more accurately. It will also help the designers, researchers and manufacturers of bearings.

Most of the previous studies have been limited to sinusoidal roughness and thick film lubrication in Rayleigh step bearing. Effect of small surface deformation due to generated pressure in thin film lubrication is significant, as it influences the performance parameters of the bearing. Different wave forms such as triangular, sawtooth, sinusoidal and square formed during finishing operations behaves differently in pressure generation. The analysis of combined effect of roughness and small surface deformation has been performed under thin film lubrication for Rayleigh step bearing using PMD as improved methods for direct iterative approach.

The purpose of this methodology is to categorise the challenges into cause and effect group. The modern scenario of customization, personalization and multi-restrictive working because of pandemics has affected the operations of manufacturing small and medium enterprises (SMEs). In the new normal, the digitalization of manufacturing SMEs can be the path breaker. Modern digitalization includes a mix of technologies such as the industrial internet of things (IIoT), the internet of things, cyber-physical system and big data analytics. This digitalization can help in achieving new design changes, efficient production scheduling, smart manufacturing and unrestricted on-time delivery of quality products. This research paper aims to recognize and analyze the challenges faced while implementing IIoT technologies in manufacturing SMEs and tries to find the possibility of mitigating challenges by blockchain technology.

There were ten challenges of IIoT implementation identified from the literature review and experts’ opinions. To collect information from Indian manufacturing SMEs, a survey tool was formed in the form of a questionnaire. On the fundament of responses received from industrial experts, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique has been used for categorizing these challenges into cause and effect groups. Further, the authors tried to mitigate observed challenges with the help of blockchain technology.

With the implementation of IIoT technologies, the manufacturing processes become conciliatory, effective and traceable in real time. Observation of the current study states that the top effect group challenges such as the security of data and reliability of technologies can be mitigated by enabling blockchain technologies. The authors conclude that blockchain-enabled IIoT technologies will be highly beneficial for the Indian SMEs strategically and practically in the current scenario.

Methodology of DEMATEL focuses on responses received from experts. The broader approach of survey from manufacturing organizations is compromised due to small sample size in this methodology. Experts approached for survey were from manufacturing SMEs of Delhi National Capital Region only. Broader survey-based techniques may be applied covering different sectors of SMEs in future work.

Technologies such as blockchain can facilitate advanced security in the application of IIoT and other such practices. While dealing with significant issues and challenges of new technologies, blockchain gives an edge of balance in the current scenario. Its properties of fixity, temper evident and circumvent fraud make this technology ideal for the digitalization of the manufacturing systems in SMEs.

Digitalization of manufacturing facilities is the need of the hour. Pandemic challenges have highlighted the urgency of it. This research will motivate and guide the manufacturing SMEs in planning strategies and long-term policies in implementing modern technologies and coping up with the pandemic challenges.

Due to the financial disturbances created by the COVID-19 pandemic and the burden on the government exchequer, it is expected to see a rise in the knowledge base of the research corpus so far as the government's fiscal sustainability is concerned. Therefore, the present research examines a systematic quantitative analysis of public debt sustainability research by applying a bibliometric approach. Research also analyzes journals, institutions, countries and authors contributing to public debt sustainability.

This paper scrutinizes the published scientific research on public debt sustainability based on the dataset of 535 articles from 1991 to 2021 obtained from the Scopus database. Biblioshiny (R-based application) and VoSviewer software were used to perform bibliometric analysis through Performance analysis and science mapping techniques. The authors combined co-citation analysis (CCA), bibliometric analysis, keyword co-occurrence analysis (KCA) and a conceptual thematic map of the most cited articles to find the intellectual structure.

The research identified three dominating clusters, e.g. fiscal sustainability and policy rules, empirical sustainability testing and debt and growth dynamics. Another finding was that most articles were analytical and empirical and few descriptive articles were found. Owing to the empirical nature of the domain, the issues concerning public debt sustainability have continued to change over the past decades for different economies, reflecting the complexity and diversity of economic structures of different economies at different times.

The insight of this article provides academicians and researchers with a more refined comprehension of the conceptual and intellectual structure of the research corpus. The present research complements the existing literature review studies by pushing the research towards emerging or less developed issues such as financial and debt crises.

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

This review has two purposes: (1) to systematically analyse the literature on export competitiveness (EC) and (2) to provide an overview of various determinants and the methodological trends in the subject field, making it possible to develop a roadmap for future researchers.

The systematic literature review (SLR) method was employed in this paper. The authors have covered three decades of research articles published in Scopus listed journals between 1991 and 2020. The determinants of EC are synthesized and widely used theories, and methodologies are identified and classified. The authors have also provided directions for future research.

The key determinants identified are labour and capital productivity, labour costs, exchange and real effective exchange rate (REER), domestic gross domestic product (GDP), trade liberalization and barriers. The findings reveal that EC is now a scientific measure, since the studies in this subject field have moved towards measuring EC and its determinants.

There has been no comprehensive review in this area exploring the theories, context, constructs and methodologies until now. Therefore, this review provides deep insights into the topic and also offers a unified picture of the subject field.

The purpose of this paper is to review the existing literature on Lean Six Sigma (LSS) for services, construct a morphological analysis (MA) framework and identify research gaps to point to future research possibilities and priorities.

The MA framework is based on literature review of 175 papers published from 2003 to 2015, across 67 journals recognised by Scopus or ABS Academic Journal Quality Guide 2015. A three-phased methodology is used by the authors, with Phase1 featuring a five-stage systematic review protocol to identify relevant journal papers for review; Phase2 presenting a framework for classifying the reviewed papers in terms of their fundamental, methodological, chronological and sector-wise orientations; and Phase3 constructing an MA framework on the classified papers and identifying the research gaps.

The MA framework constructed based on six dimensions, namely, organizational context of applications, desired outcomes, implementation systems, LSS tools and techniques, integration with other management philosophies and evaluation methods, involving 40 focused themes, has revealed 355 distinct research gaps as opportunities for future research.

This paper confirms the existence of substantial scope and points to specific topics for further research in the area of LSS for services. The findings demonstrate the gaps in academic research on the subject. In addition, the study also helps organisational leaders and practitioners to look at LSS from a holistic perspective in the services context.

The MA framework of the existing literature on LSS for services presents a unique, systematic effort to identify research opportunities. In addition, a five-stage systematic review protocol is proposed in this paper. This could be valuable to researchers and practitioners in enabling them to systematically review the literature on research subjects of interest to them.

Waste management is a crucial aspect of sustainable development, but is it economically sustainable for marginalized informal firms? The study tries to answer this question by revisiting the Porter–Wagner dilemma about the association between environmental management (EM) and firm performance (FP). The study looks into the various liquid waste management practices (LWMPs) adopted by them and the overall impact of LWMPs on firms' economic performance.

The study uses the latest available cross-sectional data source on Indian informal firms by the National Sample Survey Office (NSSO), 73rd survey round 2015–16. First, ordered logistic regression was used to analyse the factors that impact a firm's adoption of a particular LWMP. Subsequently, to capture the heterogeneity among the firms based on productivity and size, a quantile regression (QR) was employed to analyse the impact of LWMPs on firm productivity. Additionally, the propensity score matching technique was used to address endogeneity concerns.

The authors find that bigger, urban-located and female-owned firms adopt cleaner LWMPs that positively impact their economic performance. Furthermore, the QR analysis observed that the most productive firms could extract higher returns from adopting cleaner LWMPs, indicating the relevance of the Porter–Wagner dilemma, i.e. environmental and economic sustainability are possibly symbiotic, thus having a feedback mechanism.

To the authors’ limited knowledge, this is the first study analysing the relationship between EM and FP among the informal sector firms, which are away from any regulations or obligations. Since sustainability is a two-way process, policies should be devised that incentivise sustainable business practices.

The purpose of this paper is to explore the key performance indicators (PIs) that serve as a decision support tool in case of dairy supply chain practices and to analyze their interactions in the context of Indian dairy industry sector. A total of 11 PIs have been identified through the literature review and the opinions of an expert team consisting of managerial and technical experts from dairy industry and academics.

A solution methodology based on the interpretive structure modeling (ISM) technique is used to analyze the interactions among PIs and to propose a structural model. The developed model not only helps in understanding the contextual relationship among the PIs, but also in determining their interdependence to assess the supply chain performance in dairy industry. Further, the importance of PIs has been determined based on their driving and dependence power by using MICMAC analysis.

The ISM-based model suggests four PIs at first level, three PIs at second level, one PI at third level as well as one PI at fourth level and two PIs at fifth level. Model allocates to the effective information technology, brand management, responsiveness in shipment and accuracy and a control over wastages as the key PIs in the dairy industry sector. The effective traceability systems, cold chain infrastructure, quality management and the support for technological innovations are the next major PIs. There exists no autonomous PI in MICMAC analysis which proves the importance of identified PIs in the case study.

The proposed model is an attempt to capture the dynamics of milk processing sector and to incorporate all relevant constraints related to internal and external environments that would significantly improve the supply chain performance in the dairy industry.

The model developed in this study has been tested in the cooperative milk processing units based in India and also discussed with the experts from academics. This work may help practitioners, regulators and dairy industry professionals to focus their efforts toward achieving high performance by the effective implementation of the identified PIs.

In this study, 11 PIs are considered. Interactions among PIs are evaluated with the help of the ISM matrix. Out of the 11 PIs, six demonstrate both strong driving and dependence power as explained in the MICMAC analysis.