Search results

This study aims to discover the impact of the interaction between learning performance (as behavioral realism) and realistic appearance (as form realism) of AI-powered software robots on user trust. The study also aims to reveal how and why the interaction happens, especially from the dual processing perspective of affective and cognitive human responses.

We adopted an experimental research methodology in a movie recommendation system environment where machine learning-based recommendations are widely used. We conducted a 3 × 2 factorial design experimentation based on the three levels of learning performance (low, mid and high) and two levels of realistic appearance (caricature avatar and digital human). We used ANCOVA and the PROCESS Macro to analyze our models.

Our results confirm that learning performance (intelligence) is a critical factor influencing user trust in software robots, and this intelligence–trust relationship is influenced by their realistic appearance. Our results further reveal that there are two significant intermediating mechanisms, i.e. affective and cognitive user responses, and that the intelligence–appearance interaction effect on trust is explained especially by the affective response mechanism.

This study provides valuable implications for creating optimal learning performance and realistic appearance that can lead to trust in various settings where AI-powered software robots are utilized.

This study has sveral contributions to the literature. First, in addition to the well-recognized factor of anthropomorphic characteristics, this study investigates another critical behavioral factor of AI-powered robots (learning performance as intelligence characteristics) and the intriguing interaction between the two realism factors. Second, drawing upon the mediated moderation perspective, the study proposes a novice perspective on how and why the two realism factors can build user trust (the underlying mechanisms).

E-retailers face challenges in adding, engaging and retaining customers. Voice interface is a new and more inclusive modality that provides customers with a hands-free, convenient e-shopping option and is relevant for e-retailers. However, the voice interface is significantly different from the text interface that consumers are accustomed to. This study investigates customer experiences with voice interface for search and selection of products on e-commerce platforms and its subsequent impact on customer engagement and enhanced usage.

We conducted 34 in-depth interviews with executive management students. For analysis and findings, we used a grounded theory paradigm and thematic analysis.

Based on in-depth probing and analysis consumers' experiences with voice interfaces in e-commerce platforms, we identified two enablers: customer satisfaction and awe-experience that positively impacted and two inhibitors: risk perceptions and inertia that negatively impacted customer engagement and enhanced usage of voice interface.

Voice interface is transforming the customer journey in the online shopping domain. How customers experience voice interfaces when searching and selecting products on e-commerce platforms impacts their engagement with the platform and their intentions to use voice modality to interact with the e-retailer in the future. The findings substantiate tenets of dual-process theory and found that enabling and inhibiting factors are independent and can coexist. The study identifies the most salient factors that positively and negatively affect customer engagement and enhanced usage of voice interfaces.

The transition from centralized thermal power plants to distributed renewable energy sources complicates the balance between power supply and load demand in electrical networks. Energy storage systems (ESS) offer a viable solution to this challenge. This research aims to analyze the factors influencing the implementation of ESS in the Indian smart grid.

To analyze the factors affecting ESS deployment in the grid, the SAP-LAP framework (situation-actor-process and learning-action-performance) integrated with e-IRP (efficient-interpretive ranking process) was used. The variables of SAP-LAP elements were selected from expert opinion and a literature review. Here, e-IRP was utilized to prioritize elements of SAP-LAP (actors in terms of processes and actions in terms of performance).

This analysis prioritized five stakeholders in the Indian power industry for energy storage implementation: government agencies/policymakers, ESS technology developers/manufacturers, private players, research and development/academic institutions, and contractors. Furthermore, the study prioritized the necessary actions that these stakeholders must take.

The study’s findings help identify actors and manage different actions in implementing grid energy storage integration. Ranking these variables would help develop a strategic roadmap for ESS deployment and decisions about adopting new concepts.

It is one of the first attempts to analyze factors influencing ESS implementation in the power grid. Here, qualitative and quantitative methodologies are used to identify and prioritize various aspects of ESS implementation. As a result, the stakeholder can grasp the concept much more quickly.

Building a robust cold chain logistics system boosts the company’s profits in various ways. Any cold chain logistics company needs well-organised and efficient management of cold chain logistics to produce high-quality products, ensure that the product reaches the customer without any changes to the quality, and do so promptly. This paper aims to identify factors influencing cold chain logistics performance design. These factors are further helpful in analysing the behaviour intentions of stakeholders on increasing the cold chain logistic performance.

The authors conducted a thorough literature review to identify the variables that affect the performance of the cold chain logistics design. The factors were identified using exploratory factor analysis and empirically analysed using confirmatory factor analysis. The study also used structural equation modelling (SEM) to examine cold chain logistics performance determinants. Data was collected from 380 respondents working in the cold chain.

This study selected the factors influencing CCL performance, including five main factors and 22 sub-factors. Distribution, warehouse inventory storage, quality, demand, and technology affect the CCL’s performance. The results confirmed the theoretical model and proved that the factors significantly positively impact CCL performance.

Future studies should focus on actual case studies to confirm the usefulness of the parameters found, examine how they affect performance growth, provide important insights into how to improve overall business performance and assist in identifying crucial research hotspots.

The study provides insight into issues regarding performance development in cold chain logistics for various stakeholders associated with the cold chain logistics industry, including practical managers, academics, and consultants. It also argues in favour of giving problems with CCL performance a higher priority. Policymakers interested in the service sector, like the Indian Department of Commerce and MSMEs, make up a modest additional audience for this work.

Indian meat industry can be organised by implementing this methodology. This work benefits the government to get more transparent transaction and data digitalisation, which comes into account of GST.

There is a lack of significant quantitative literature suggesting modification strategies for factors affecting processed meat and chicken products in storage and transportation levels in India. Thus, this work tried to fill this gap and add the food chain logistics literature that helps practitioners and scholars enhance the food supply chain in developing countries.The framework developed for this study is where its originality lies. A detailed examination of cold chain logistics is included in the paper.

This study aims to focus on exploring the role of fear of missing out (FOMO) in the technology adoption context, whereby the bottom-of-the-pyramid (BOP) segment was studied to explore the factors responsible for the development of behavioural intentions (BI) to use unified payments interface (UPI), a disruptive technological phenomenon in the mobile payment systems field.

A mixed-method research approach involving both qualitative and quantitative methods was used. Initially, qualitative data obtained through interviews with UPI’s BOP users were subjected to thematic analysis, leading to the identification of eight factors and the construction of a conceptual model. Subsequently, 354 responses were gathered, and empirical analysis was conducted using structural equation modelling in AMOS 23.0.

Eight factors, including personal and social benefits, perceived security risk, socio-cultural influences, governmental influence, usability, psychological inertia, perceived value (PV) and FOMO, were discovered. The quantitative examination confirmed the validity of the conceptual model in the BOP context, explaining 51% of the variance in BI. FOMO and PV emerged as robust indicators of UPI adoption, with PV significantly regulating the impact of FOMO.

To the best of the author’s knowledge, this study is one of the first to explore what drives BOP users in an emerging economy to adopt UPI. The conceptual model it presents contributes to the advancement of technology adoption literature by incorporating FOMO alongside newly identified BOP-specific factors.

The adoption of metaverse in manufacturing holds significant benefits, but there are several barriers to its seamless integration. This paper aims to identify such barriers and prioritize them in a manner that allows industrial leaders to strategize for smooth adoption.

In this study, we applied two-stage methods, first the identification and validation of barriers through an empirical study applied to Exploratory Factor Analysis (EFA). A purposive sampling technique and snowball sampling facilitated data collection from these expert sources. Through snowball sampling, additional contacts working in the metaverse field were reached, resulting in 235 possible respondents; the survey yielded 104 completed responses. Thereafter, the best-worst method (BWM) was used to measure and rank the barriers.

The study results show that the two most critical barriers are “Lack of data security and privacy” and “Lack of integration compatibility with existing systems.” Such findings inform industry leaders of specific recommendations for structural changes, training programs, necessary technological investments and collaborative efforts to overcome these barriers.

This work adds significantly to academic discussion by prioritizing barriers towards integrating metaverse technology in manufacturing. In addition, this strategic methodology aids in critical appraisal and ranking of barriers for successful adoption. This study also identifies key barriers but acknowledges that other unexamined factors might be lurking in the background, such as virtual economy, financial risks and cross-border legal issues.

The study’s conclusions cannot be generalized to the other sectors, thus indicating the necessity of carrying out a comparative multi-sector study in the future.

To the best of the authors’ knowledge, the study on systematic prioritization of barriers to adopting metaverse technology in manufacturing is the original contribution of the authors.

Made-in-India tyres are rapidly integrating into global supply chains due to a globally coordinated regulatory environment and aspiring for value creation and contribution to circularity. However, it is not clear what are the constituent components of emerging technologies like blockchain that can facilitate value creation and how they are associated. Therefore, this study intends to explore the elements of blockchain technology and how they create value to bring circularity to a tyre supply chain.

This study employed a survey-based quantitative methodology to test the theoretical framework of blockchain-enabled circular supply chains using non-parametric regression analysis. A total of 307 responses from India-based supply chain professionals’ data were collected from September 2022 to January 2023 to perform non-parametric regression analysis.

The results indicated that blockchain could improve visibility and accessibility by having value-creation capabilities of data collection, monitoring, processing and analysis to facilitate the circular tyre supply chain which focuses on recycling, reusing, reducing and rethinking initiatives. Further, accessibility contributes more than visibility to creating value toward a blockchain-enabled tyre circular supply chain.

First, this study employs a grounded theory-driven approach in identifying and testing a framework through hierarchical regression. This study identifies the role of blockchain technology in unfolding visibility and accessibility towards value creation, enhancing circularity in the supply chains in complex and critical industries such as tyres in India and across the globe.

Natural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption, which is achieved by developing supply chain factors with Artificial Intelligence (AI) and Big Data Analytics (BDA).

This research examines how organizations using AI and BDA can bring resilience to supply chains. To achieve the objective, the authors developed the methodology to gather useful information from the literature studied and developed the Total Interpretive Structural Modeling (TISM) by consulting 44 supply chain professionals. The authors developed a quantitative questionnaire to collect 229 responses and further test the model. With the analysis, a conceptual and comprehensive framework is developed.

A major finding, this research advocates that supply chain resilience is contingent upon utilizing supply chain analytics. An empirical study provides further evidence that the utilization of supply chain analytics has a positive and favorable effect on the flexibility of demand forecasting to inventory management, resulting in increased efficiency.

Few studies demonstrate the impact of advanced technology in building resilient supply chains by enhancing their factors. To the best of the authors' knowledge, no earlier researcher has attempted to infuse AI and BDA into supply chain factors to make them resilient.

Industries have been the most significant contributor to carbon emissions since the beginning of the Industrial Revolution. The transition to Industry 5.0 (I5.0) marks a pivotal moment in the industrial revolution, which aims to reconcile productivity with environmental responsibility. As concerns about the decline of environmental quality increase and the demand for sustainable industrial methods intensifies, experts recognize the shift toward the I5.0 transition as a crucial turning point.

This review study explores the convergence of green technological advancements with the evolving landscape of I5.0, thereby presenting a roadmap toward carbon neutrality. Through an extensive analysis of literature spanning from 2012 to 2024, sourced from the Scopus database, the research study unravels the transformative potential of green technological innovations, artificial intelligence, green supply chain management and the metaverse.

The findings underscore the urgent imperative of integrating green technologies into the fabric of I5.0, highlighting the opportunities and challenges inherent in this endeavor. Furthermore, the study provides insights tailored for policymakers, regulators, researchers and environmental stakeholders, fostering informed decision-making toward a carbon-neutral future.

This review serves as a call to action, urging collective efforts to harness innovation for the betterment of industry and the environment.

This study aims to provide an analysis of cyber-physical system (CPS)-based lean tools. This study focuses on the identification of lean tools for integration with CPS and analyzes those tools using MCDM (multi-criteria decision-making) approaches.

There exists a need to integrate lean manufacturing with Industry 4.0 technologies. According to literature analysis, CPS is the first stage to implement Industry 4.0 technologies. Based on the extensive study, six CPS-based lean tools, i.e. CPS-based Jidoka system, CPS-based Kanban, CPS-based Andon support system, CPS-based Just-in-time delivery system, CPS-based poka-yoke cell and CPS-based value stream mapping have been considered; then Grey TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) MCDM technique has been applied to rank those tools. These CPS-based lean tools are ranked based on seven performance measures as recognized by academic and industry experts.

The top three CPS-based lean tools are CPS-based Kanban 4.0, CPS-based value stream mapping and CPS-based Just-in-time delivery system have been selected based on the above ranking. The study results have been validated using grey-based approach.

Appropriate criteria to evaluate significant lean tools for integration with CPS are identified, which facilitates managers to assess their current tools and technologies that could be integrated with I4.0, and the implementation of CPS-based lean tools would improve organizational performance.

In the emerging Industry 4.0, integration with advanced technologies provides high degrees of optimization. But there exist challenges for industries to integrate CPS with lean tools; hence, this study attempts to identify and analyze CPS-based lean tools. The lean tools are ranked for integration with CPS, the problem is modeled as MCDM problem, and the obtained results are again validated using grey approach. Prioritizing lean tools for integration with CPS is the original contribution of this study.