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This study aims to conduct a systematic literature review and bibliometric analysis on the topic of digital labour. The study also identifies the future research directions for the topic.

In total, 118 research papers were identified and reviewed from 11 established research databases and A*, A and B category journals from the ABDC journal list. The papers covered a timespan between 2006 and 2023. Bibliometric analysis was conducted to identify key research hotspots.

The emergent themes and associated sub-themes related to digital labour were identified from the literature. The paper found three significant themes that include digital labour platform, gig economy and productivity. This study also acts as a platform to initiate further research in this field for academicians, scholars, industry practitioners and policymakers. The future research scope in the topic is also presented.

The present study is unique in its nature as it approaches the topic of digital labour from all relevant perspectives.

We aim to analyze the capital structure heterogeneity for manufacturing and service sector firms. Additionally, we analyze the impact of the COVID-19 pandemic on the leverage adjustments of corporate firms.

This study applies the two-step system generalized method of moments (system-GMM) and panel data of 1,115 manufacturing and 482 service sector firms listed with the Bombay Stock Exchange (S&P BSE) from 2010 to 2023. We developed and analyzed three models. Model 1 analyzes the leverage determinants and speed of adjustment (SOA) for the manufacturing and service sectors. Model 2 evaluates the leverage SOA for various sub-sectors, and Model 3 analyzes the impact of the COVID-19 pandemic on the leverage SOA.

This study suggests the three following. First, the direction of leverage determinants suggests that manufacturing firms are highly tangible. In contrast, service sector firms are high-growth firms and recorded a higher SOA (12.01%) than manufacturing (9.09%). Second, analyzing the leverage heterogeneity, we found that SOA varies across the sub-sectors. For manufacturing, food and beverage sub-sector recorded the highest SOA (12.58%), while consumer durables reported the lowest (6.38%). Communication recorded the highest (24.15%) for services, while industrial services recorded the lowest (11.18%). Third, firms across sectors and sub-sectors increased their SOA during COVID-19 pandemic.

This in-depth analysis of leverage heterogeneity for different sectors and subsectors will assist policymakers, corporate managers and other stakeholders in making agile financial decisions.

The analysis of leverage heterogeneity for the manufacturing and service sector from the emerging Indian economy marks a novel contribution to existing literature.

The main purpose of this paper is to investigate drivers and barriers of climate change mitigation strategies (CCMS), their linkages and impact in Indian Iron and Steel Industry (IISI) in light of ninth sustainable development goal (building resilient infrastructure, promote sustainable industrialization and foster innovation).

To identify relevant drivers and barriers, a thorough literature review and opinions of industry experts were obtained. Utilizing Total Interpretive Structural Modeling (TISM), the selected drivers and barriers were modeled separately along with Cross Impact Matrix-multiplication Applied to Classification (MICMAC).

Pragmatic and cost-effective technology, less supply chain complexity, robust policy and legal framework were found to have the highest driving power over all the other drivers. Findings suggest political pressure as the most critical barrier in this study. The results from TISM and MICMAC analysis have been used to elucidate a framework for the understanding of policymakers and achieve top management commitment.

This paper will help researchers, academicians, industry analysts and policymakers in developing a systems approach in prioritizing CCMS in energy-intensive (coal dependent) iron and steel plants. The model outcomes of this work will aid operational research to understand the working principles in other industries as well.

To the best of authors' knowledge, there is paucity of reported literature for the drivers and barriers of CCMS in iron and steel industry. This paper can be considered a unique, first attempt to use data from developing nations like India to develop a model and explain relationships of the existing drivers and barriers of CCMS.

This study aims to explore the reasons for the rise of independent, stand-alone restaurants and ascertains the benefits of outsourcing food and beverage (F&B) in luxury hotels in India from the perspectives of the strategic partners involved in such an alliance. The study also proposes different formats for F&B outsourcing in luxury hotels.

An exploratory study was carried out by collecting primary data from 16 Hotel General Managers and F&B operations experts through qualitative, semi-structured, personal and in-depth interviews. NVivo12 software was used to carry out a qualitative thematic analysis of the data. The primary data collected were triangulated with secondary data gathered through literature review of academic papers, industry reports and studies on the trends of restaurants in luxury hotels being outsourced.

The study focusses on the antecedents of the rise of stand-alone restaurants in the Indian hospitality industry. To combat the competitive disruption arising because of this trend, the study posits the business model innovation of outsourcing F&B operations in luxury hotels.

The benefits of a strategic alliance from the perspective of both parties – the luxury hotel and Michelin-star chef or branded/marquee restaurant – are elucidated. Further, three broad formats, which can be adopted for speciality restaurant outsourcing are also proposed. Practitioners, researchers and educationists in the hospitality industry would find the implications of this study useful in the context of the present customer-centric business environment where hotels are constantly striving to meet the exponentially rising bar of guest expectations in an increasingly globalised milieu.

The study proposes a preliminary road map for internationalisation of F&B operations through the business model innovation of outsourcing operations of in-house specialty restaurants by luxury hotels in the Indian context.

This study aims to quantify and prioritize the financial performance (FP) determinants in Indian small and medium-sized enterprises (SMEs).

Analytic hierarchy process, a multi-criteria decision-making tool, was used. Experts were allowed to express the opinion regarding the relative importance of each factor and sub-factors by making pairwise comparisons through a structured questionnaire based on a nine-point scale.

Market orientation (0.4529) was perceived as the most important FP determinant followed by the entrepreneurial orientation (0.3382) and corporate social responsibility (0.2089) in SMEs.

This study can be considered as a pilot study because it is confined to Indian SMEs. Future research studies can incorporate the opinion or insights of other stakeholders and may target the SMEs situated in different geographical areas.

The inferences drawn in this study would clarify the conceptual and contextual applicability of competitive strategies in SMEs. Indeed, proposed hierarchy and developed framework would guide the SMEs in strategic planning. Moreover, it would help in repositioning and alignment of core strategies duly with business objectives.

The study represents the foremost step and a unique effort in the area of development of hypothetical model (a hierarchal model) with the framework considered to prioritize the FP determinants in SMEs.

The COVID-19 pandemic and the subsequent lockdown have hit the food service industry very hard. The COVID-19 outbreak has created a sharp downturn for firms in the food service industry, compelling actors across the whole food service supply chain to rethink their strategies. The purpose of this paper is to document the impact of COVID-19 on the food service supply chain, as well as to identify crisis management strategies food service firms use during the hectic early phase of the COVID-19 pandemic to survive the current and prepare for future pandemics.

We performed a qualitative descriptive study using 21 semi-structured interviews with actors across the food service supply chain (i.e. farmers, wholesalers and food service providers). Data were collected to shed light on food service firms' decision making during the hectic early phase of the COVID-19 pandemic to uncover various crisis management strategies used.

By integrating the disaster and crisis pyramid and resilience theory, four core crisis management strategies to respond to the COVID-19 pandemic are conceptualized, i.e. (1) managing resources, (2) diversifying strategically, (3) prioritizing long-term outcomes and (4) bonding socially.

The theoretical contributions include documenting the performance impact of the COVID-19 pandemic on the food service supply chain and exploring crisis management strategies food service firms employed during the hectic early phase of the COVID-19 pandemic. Thus, functioning and survival during a pandemic, an emerging field in literature, are central to this study. Additionally, while recent research suggests that integrating crisis management and resilience literature may provide a more complete understanding of the organization–crisis relationship, these literature streams mainly developed in isolation. By integrating the literature streams of crisis management and resilience and applying these theories to the COVID-19 crisis, our study provides specific managerial guidelines.

Despite the recognized role of motivation of actors in technology adoption decisions, there is limited understanding of the psychological processes underlying the motivation. The purpose of this paper is to explore this gap by investigating Building Information Modeling (BIM) adoption from the viewpoint of Maslow’s motivational theory on hierarchy of needs.

This research uses mixed methods. Initially theoretical arguments establish the suitability of Maslow’s hierarchy of needs as the conceptual framework to investigate technology adoption. The hypotheses and research questions are investigated using data collected through focus group interviews, interviews and field observations in Australian architecture engineering and construction (AEC). The findings are validated with a survey of BIM adoption cases reported in literature, and additional interviews conducted in Finnish AEC sector. Finally, abductive reasoning is applied to seek the best possible explanation for the observed patterns.

It is found that besides individuals, organizations also demonstrate hierarchical ordering of innovation-related needs. Three broad categories of innovation-related needs are identified. Using abduction, the innovation-related needs of actors are described in terms of stable and excited states.

The findings are primarily based on studies conducted in regions with developed economies.

This research shows that Maslow’s hierarchy of needs could be a useful diagnostic framework to assess actors’ response towards technology adoption.

This investigation into the potential usefulness of Maslow’s theory into understanding technology adoption is by itself a novel research contribution. The finding that hierarchical view of needs can partly explain the adoption decisions of both individual and organizational actors is an original contribution.

This study aims to examine empirically the nature of supply chain disruptions caused by the COVID-19 pandemic, particularly on the Indian automobile sector.

The authors evaluate the stock market performance of individual company and its quantitative relationship to certain variables related to company’s supply chain.

The authors analysed the company’s operations considering several ratios like asset intensity, company size, labour intensity and inventory to revenue.

The results of analysis can help the companies to understand how disruptions in the supply chain can affect the company’s operations and how it is perceived by the investors in the stock market.

Also, investors are benefitted, as they can understand how different companies with different operational characteristics react to global disruptions in supply chains, which in turn would help them to find better investment opportunities.

Although there is some literature available on the qualitative as well as quantitative analysis, the authors go further to analyse the impact of supply chain disruption on the stocks of the automobile sector.

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates.

The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R²) and mean absolute error (MAE).

Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments.

The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study.

This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.

This study explores the factors impacting user adoption and trust in blockchain-based food delivery systems, with a spotlight on the Open Network for Digital Commerce (ONDC). In the evolving food delivery sector, blockchain offers transparency and efficiency. Through the Unified Theory of Acceptance and Use of Technology (UTAUT) lens, this research provides insights for businesses and policymakers, highlighting the importance of blockchain’s integration into food delivery.

The research employed the UTAUT and its extensions as the theoretical framework. A structured questionnaire was developed and disseminated to users of the ONDC platform, and responses were collected on a seven-point extended Likert scale. The analyses were undertaken employing the partial least squares (PLS) methodology and structural equation modelling (SEM).

Key factors like performance expectancy, effort expectancy and social influence were found influential for adoption. Trust played a central role, while perceived risk didn’t significantly mediate the adoption process. Digital culture didn’t significantly moderate the adoption intention.

This research adds to the existing body of knowledge by providing empirical insights into user adoption and trust in blockchain-based food delivery platforms. It is among the pioneer studies to apply the UTAUT model in the realm of blockchain-based food delivery platforms, thereby offering a unique perspective on the dynamics of user behaviour in this emerging field.