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The purpose of this paper is to present an empirical investigation of maintenance performance (MP) management practices from the sugar industry in India.

Empirical data for this study were collected through field visits, interviews and published reports. Statistical methods including correlation, multiple regression and cluster analysis are utilized to accomplish the objective of the study.

Explanation with multiple regression analysis showed that the sugar industry MP is significantly and positively related to maintenance approach (MA), continuous improvement (CI), financial approach and spare part management (SPM). Cluster analysis showed that sugar industries focusing on MA, CI and policy development and organization are having higher MP. The cluster analysis also pointed out that there is a substantial variation in MP due to the type of ownership (private and cooperative) while no variation has been observed due to installed capacity (low and high).

The generalization of the results obtained in this work for the sugar industry can be possible through a larger sample size.

The study contributes to the better understanding of maintenance measures in the sugar industry and provides insights on the role of maintenance managerial practices in enhancing the MP.

The findings provide empirical evidence that maintenance practices across the sugar industry are important to improve MP.

The new ways to complete financial transactions have been developed by setting up mobile payment (m-payment) platforms and such platforms to access banking in the financial mainstream can transact as never before. But, does m-payment have veiled consequences? To seek an answer, the research was undertaken to explore the dark sides of m-payment for consumers by extending the theory of innovation resistance (IR) and by measuring non-adoption intention (NAI).

Three hundred individuals using popular online m-payment apps such as Paytm, PhonePe, Amazon Pay and Google Pay were surveyed for the primary data. IBM AMOS based structural equation modelling (SEM) was used to analyse the data.

Each m-payment transaction leaves a digital record, making some vulnerable consumers concerned about privacy threats. Lack of global standards prevents consumers from participating in the m-payment system properly until common interfaces are established based on up-to-date standards. Self-compassion (SC) characteristics such as anxiety, efficacy, fatigue, wait-and-see tendencies and the excessive choice of technology effect contribute to the non-adoption of m-payment.

This study proposes a threat model and empirically explores the dark sides of m-payment. In addition, it also unveils the moderator's role of SC in building the structural relationship between IR and NAI.

The Internet is used as a tool to seek health information by individuals. Mental health concerns are the high prevalence of the novel coronavirus disease 2019 (COVID-19) and preventive steps are required to curb the illness. Therefore, to gain more insight into health concerns, it is now a common practice to seek health information on the Internet. This study propose an integrated theoretical model to explore the relationship between COVID-19 protocols and perceived online trust with online health information seeking intention (OHISI) and a moderating effect of perceived severity and perceived urgency.

Data are collected from 325 athletes in the category of individual and team sports through an online survey in a Likert-scale questionnaire. The analysis is performed with a quantitative methodology.

The study reveals the bright side of online health information (OHI), which brings athletes together and has played out with virtual happy hours, meetings and events. The bright side of OHI reflects social, cultural, technological and economic benefits. An OHI chatbot offers bright personalised side information to the individual seeker, which is more convenient and efficient than human capabilities.

The pivotal contribution is the integrated theoretical framework that is derived from multidisciplinary literature to capture the complexity of OHI. Also, it conceptualises the constructs in the context of OHI and COVID-19.

This study aims to investigate and compare the influence of various fiber types (polypropylene, steel and glass) on the workability, mechanical properties, ductility, impact resistance, durability and microscopic properties of geopolymer concrete (GPC) with conventional concrete (CC).

The CC and GPC of M40 grade were incorporated with an optimum 1% of fibers and superplasticizers were added in a ratio of 2% by weight of the geopolymer binder. The slump cone and compaction factor tests were performed to analyze the workability. To evaluate the mechanical performance of GPC, the compressive strength (CS), split tensile strength (STS), flexural strength (FS) and modulus of elasticity (MOE) tests were performed. A falling weight impact test was performed to determine the impact energy (IE) absorbed, the number of blows for initial cracking, the number of blows for complete failure and the ductility aspect.

Fibers and superplasticizers significantly improve GPC properties. The study found that fibers reduce the brittleness of concrete, improving the impact and mechanical strength compared to similar-grade CC. The steel fibers-reinforced GPC has a 15.42% higher CS than CC after three days, showing a faster CS gain. After 28 days, GPC and CC have MOE in the range of 23.9–25.5 GPa and 28.8–30.9 GPa, respectively. The ultimate IE of the GPC with fibers was found to be 5.43% to 21.17% higher than GPC without fibers.

The findings of the study can be used to explore different combinations of raw materials and mix designs to optimize the performance of GPC.

Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy is required for health coverage tailored to needs and capacity. Therefore, this study aims to explore the adoption of a cognitive computing decision support system (CCDSS) in the assessment of health-care policymaking and validates it by extending the unified theory of acceptance and use of technology model.

A survey was conducted to collect data from different stakeholders, referred to as the 4Ps, namely, patients, providers, payors and policymakers. Structural equation modelling and one-way ANOVA were used to analyse the data.

The result reveals that the behavioural insight of policymakers towards the assessment of health-care policymaking is based on automatic and reflective systems. Investments in CCDSS for policymaking assessment have the potential to produce rational outcomes. CCDSS, built with quality procedures, can validate whether breastfeeding-supporting policies are mother-friendly.

Health-care policies are used by lawmakers to safeguard and improve public health, but it has always been a challenge. With the adoption of CCDSS, the overall goal of health-care policymaking can achieve better quality standards and improve the design of policymaking.

This study drew attention to how CCDSS as a technology enabler can drive health-care policymaking assessment for each stage and how the technology enabler can help the 4Ps of health-care gain insight into the benefits and potential value of CCDSS by demonstrating the breastfeeding supporting policy.

Coronavirus disease (COVID-19) was declared as a pandemic since COVID-19's widespread outbreak and the hospitality industry has been the hardest hit due to lockdown. Consequently, hospitality workers are suffering from the negative aspects of mental health. In the event of such a crisis, this study aims to explore the link between unemployment and home isolation to the willingness to choose electronic consultation (e-consultation) by exploiting psychological ill-being and behavioural intention (BI) with marital status as a moderator.

A quantitative methodology is applied to primary data collected from 310 workers from the hospitality industry through an online survey.

Findings of this study suggest that the usage of the e-consultation service can be adopted using three levels. There are valid reasons to conclude unemployment and home isolation are linked to higher rates of psychological health behaviours, which can result in stigma, loss of self-worth and increased mortality. The adverse effect is higher for single individuals than for married people.

The study focussed on e-consultation, BI coupled with the Fishbein scale and a classification model for the prediction of willingness to choose e-consultation with the extension of Theory of Planned Behaviour (TPB).

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.

Co-creation of services (CCOS) is a collaborative strategy that emphasises customer involvement and their expertise to increase the value of the service experience. In the service ecosystem, artificial intelligence (AI) plays a key role in value co-creation. Therefore, this study is undertaken to empirically uncover how AI can empower CCOS.

The source data were collected from 305 service provider respondents and quantitative methodology was applied for data analysis.

New service development augmented with AI provides tangible value to service providers while also providing intangible value to supportive customers. With AI, service providers adapt to new innovations and enrich additional information, which eventually outperforms human-created services.

AI adoption for CCOS empowerment in service businesses brings “service-market fit”, which represents the significant benefits wherein customers contribute to creativity, intuition, and contextual awareness of services, and AI contributes to large-scale service-related analysis by handling volumes of data, service personalisation, and more time to focus on challenging problems of the market.

This study presents theoretical concepts on AI-empowered CCOS, AI technological innovativeness, customer participation in human-AI interaction, AI-powered customer expertise, and perceived benefits in CCOS, and subsequently discusses the CCOS empowerment framework. Then, it proposes a novel conceptual model based on the theoretical concepts and empirically measures and validates the intention to adopt AI for CCOS empowerment. Overall, the study contributes to novel insight on empowering service co-creation with AI.

The disconnect between producers and consumers is a fundamental issue causing irregularities, inefficiencies and leakages in the agricultural sector, leading to detrimental impacts on all stakeholders, particularly farmers. Despite the potential benefits of Metaverse technology, including enhanced virtual representations of physical reality and more efficient and sustainable crop and livestock management, research on its impact in agriculture remains scarce. This study aims to address this gap by identifying the critical success factors (CSFs) for adopting Metaverse technology in agriculture, thereby paving the way for further exploration and implementation of innovative technologies in the agricultural sector.

The research employed integrated methodology to identify and prioritise critical success criteria for Metaverse adoption in the agricultural sector. By adopting a mixed-method technique, the study identified a total of 15 CSFs through a literature survey and expert consultation, focusing on agricultural and technological professionals and categorising them into three categories, namely “Technological”, “User Experience” and “Intrinsic” using Kappa statistics. Further, the study uses grey systems theory and the Ordinal Priority Approach to prioritise the CSFs based on their weights.

The study identifies 15 CSFs essential for adopting Metaverse technology in the agricultural sector. These factors are categorised into Technological, User Experience-related and Intrinsic. The findings reveal that the most important CSFs for Metaverse adoption include market accessibility, monetisation support and integration with existing systems and processes.

Identifying CSFs is essential for successful implementation as a business strategy, and it requires a collaborative effort from all stakeholders in the agriculture sector. The study identifies and prioritises CSFs for Metaverse adoption in the agricultural sector. Therefore, this study would be helpful to practitioners in Metaverse adoption decision-making through a prioritised list of CSFs in the agricultural sector.

The study contributes to the theory by integrating two established theories to identify critical factors for sustainable agriculture through Metaverse adoption. It enriches existing literature with empirical evidence specific to agriculture, particularly in emerging economies and reveals three key factor categories: technological, user experience-related and intrinsic. These categories provide a foundational lens for exploring the impact, relevance and integration of emerging technologies in the agricultural sector. The findings of this research can help policymakers, farmers and technology providers encourage adopting Metaverse technology in agriculture, ultimately contributing to the development of environment-friendly agriculture practices.

This study aims to compare the short-run performance of construction and non-construction initial public offerings (IPOs) that are offered in India during 2006–2015. The study also attempts to investigate the impact of ownership structure (i.e. concentrated ownership in the hand of promoters and institutional ownership) and market sentiment on the performance of construction sector IPOs in short run.

A total of 281 IPOs were listed at National Stock Exchange, India, during the study period, and 44 of those were from construction sector. The short-run performance of these construction and non-construction IPOs was compared using two indicators, i.e. monthly stock return (SR) and excess return over market benchmark (MAR). To examine the effect of concentrated ownership in the hand of promoters, institutional ownership and market sentiment on IPO performance, systematic dynamic panel regression model was developed.

The IPOs of construction firms perform significantly better than the non-construction firms. The performance of construction IPOs is significantly driven by the lag effect. This suggests a significant informational inefficiency, which results in a delayed reaction by investors. The market sentiment has a positive influence on the performance of construction sector IPOs, whereas the institutional holding has a negative influence.

To the best of the author’s knowledge, this study is the first attempt to examine the performance of construction sector IPOs in short run. The study uses systematic dynamic panel data regression, which provides better and reliable estimates.