Search results

This study investigates augmented reality (AR) retailing and attempts to develop a profound understanding of consumer decision-making processes in AR-enabled e-retailing.

The study is grounded in rich informational cues and information processing mechanisms by incorporating the elaboration likelihood model (ELM) and trust transfer theory. This study employs a mixed analytic method that incorporates structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to provide a complete picture of individual information process mechanisms in AR retailing under the tenet of ELM.

The SEM analysis results confirm the relationships between the central and peripheral route factors, information processing outcomes and eventual behavioral intentions. Moreover, all configurations revealed by the fsQCA include both central and peripheral factors. Hence, the dual routes proposed in the ELM are verified by using two distinct analytical approaches.

This study is pioneering in validating and contextualizing ELM theory in AR retailing. In addition, this study offers a methodological paradigm by demonstrating the application of multi-analysis in exploring consumers’ information process mechanisms in AR retailing, which offers a holistic and comprehensive view to understand consumers’ decision-making mechanisms.

Technology 4.0 comes with a challenge to understand the degree of users’ willingness to adopt a digital transformation. Metaverse, being a digital transformation, enables real-world activities in the virtual environment, which attracts organizations to adopt the new fascinating technology. This paper thus explores the uses and gratification factors affecting user adoption and recommendation of metaverse from the management perspective.

The study adopts a mixed approach where structural topic modeling is used to analyze tweets about the metaverse, and the themes uncovered from structural topic modeling were further analyzed through data collection using structural equation modeling.

The analyses revealed that social interaction, escapism, convenient navigability, and telepresence significantly affect adoption intent and recommendation to use metaverse, while the trendiness showed insignificance. In the metaverse, users can embody avatars or digital representations, users can express themselves, communicate nonverbally, and interact with others in a more natural and intuitive manner.

This paper contributes to research as it is the first of its kind to explore the factors affecting adoption intent and recommendation to use metaverse using Uses and Gratification theory in a mixed approach. Moreover, the authors performed a two-step study involving both qualitative and quantitative techniques, giving a new perspective to the metaverse-related study.

India liberalized its economy in 1991, which resulted in intense global competition, quality-conscious and demanding customers. Additionally, significant technological advancements lead to enhancements in products and processes. These forced Indian organizations to adopt innovative business strategies in the past 30 years. Meanwhile, the Lean Six Sigma methodology has significantly grown with vast applicability during the past 30 years. Thus, the purpose of this study is to develop the learning on Lean Six Sigma methodology in the Indian context through investigation of literature.

A three-stage systematic literature review approach was adopted to investigate the literature during the present study. In total, 187 articles published in 62 journals/conference proceedings from 2005 to 2022 (18 years) were shortlisted. The first part of the article summarizes the significant milestones towards the quality journey in the Indian context, along with the evolution of the Lean Six Sigma methodology. The second part examines the shortlisted papers on Lean Six Sigma frameworks, their applicability in industrial sectors, performance metrics, outcomes realized, publication trends, authorship patterns and leading researchers from the Indian perspective.

Lean Six Sigma has emerged as a highly acclaimed and structured business improvement strategy worldwide. The Indian economy has seen remarkable growth in the past decade and is one of the fastest-growing economies in the 21st century. Lean Six Sigma implementation in India has significantly increased from 2014 onward. The study revealed that researchers have proposed several different frameworks for Lean Six Sigma implementation, the majority of which are conceptual. Furthermore, the balanced applicability of Lean Six Sigma in manufacturing and service sectors was observed with the highest implementation in the health-care sector. Additionally, the widely adopted tools, techniques along with performance metrics exploring case studies were reported along with a summary of eminent and leading researchers in the Indian context.

This study is confined to reviewed papers as per the research criteria with a significant focus on the Indian context and might have missed some papers due to the adopted papers selection strategy.

The present study is one of the initial attempts to investigate the literature published on Lean Six Sigma in the Indian context, including perspective on the Indian quality movement. Therefore, the present study will provide an understanding of Lean Six Sigma methodology in the Indian context to graduating students in engineering and management and entry-level executives. The analysis and findings on Lean Six Sigma frameworks, research approach, publications details, etc., will be helpful to potential research scholars and academia. Additionally, analysis of case studies on Lean Six Sigma implementation by Indian industries will assist the managers and professionals in decision making.

The purpose of this study is to propose a research model based on the stimulus–organism–response (S–O–R) model to examine whether network externality, personalization and sociability as environmental feature antecedents to learners’ learning engagement (LE) can influence their learning persistence (LP) in massive open online courses (MOOCs).

Sample data for this study were collected from learners who had experience in taking MOOCs provided by the MOOC platform launched by a well-known university in Taiwan, and 371 usable questionnaires were analyzed using structural equation modeling in this study.

This study proved that learners’ perceived network externality, personalization and sociability in MOOCs positively affected their cognitive LE, psychological LE and social LE elicited by MOOCs, which jointly led to their LP in MOOCs. The results support all proposed hypotheses, and the research model accounts for 76.2% of the variance in learners’ LP in MOOCs.

This study uses the S–O–R model as a theoretical base to construct learners’ LP in MOOCs as a series of the inner process, which is affected by network externality, personalization and sociability. It is worth noting that three psychological constructs including cognitive LE, psychological LE and social LE are used to represent learners’ organismic states of MOOCs usage. To date, hedonic/utilitarian concepts are more often adopted as organisms in previous studies using the S–O–R model, and psychological constructs have received lesser attention. Hence, this study’ contribution on the application of capturing psychological constructs for completely expounding three types of environmental features as antecedents to learners’ LP in MOOCs is well documented.

This paper aims to propose a structural sizing approach of an unmanned aerial vehicle (UAV) wing that takes into account the aeroelasticity effects through a fluid–structure interaction analysis.

The sizing approach proposed in this study is an iterative process, each iteration of which consists of two sub-loops, a multidisciplinary analysis (MDA) loop followed by a structural optimization loop. The MDA loop seeks the aeroelastic equilibrium between aerodynamic forces and structural displacements using a fixed-point iteration scheme. Once the equilibrium is reached, the converged pressure loads are used for the structural optimization, which aims to find the structural thicknesses that minimize the wing weight under failure criteria. The two sub-loops are run sequentially in an iterative process until the mass is converged. The analysis models are implemented in open-source software, namely, PANUKL for aerodynamics and MYSTRAN for structures, while the whole process is automated with Python and integrated in the open-source optimization framework OpenMDAO.

The approach was applied to the design of the Predator MQ-1 wing. The results of the MDAs show the convergence of the wing deformations to the flight shape after few iterations. At the end of the aeroelastic sizing loop, the result is a structurally sized wing with minimal weight considering the aeroelasticity effects.

The approach proposed takes into account the wing aero-structural coupling effects while sizing its structure instead of a fixed load distribution. In addition, the approach is fully based on open-source codes, which are freely available for public use and can be fully reproducible.

This study aims to evaluate the structural oil shocks effect on stock returns of Indian renewable energy companies across market conditions.

This study applies the structural vector autoregression model to estimate sources of oil shocks such as oil supply shock, aggregate demand shock and oil price-specific demand shock. In the next step, the panel quantile regression model estimates the effect of these oil shocks on stock return across market conditions. Monthly data are collected from January 2009 to December 2019. All renewable energy companies listed on the National Stock Exchange of India are considered for the analysis.

In the whole sample analysis, this study finds that oil shocks negatively affect stock returns in most of the market conditions except oil price-specific demand shock. In sub-groups, oil shocks driven by supply and aggregate demand also negatively affect stock return in most market conditions. This study finds the positive interaction of oil price-specific demand shock. A majority of these positive interactions happen in bearish market conditions. In the whole sample, the asymmetric effects of shocks driven from oil supply and oil price-specific demand are seen in most quantiles or market conditions. At the same time, aggregate demand shock does not affect asymmetrically. In the sub-group analysis, standalone renewable energy companies stock returns are least asymmetrically affected by these oil shocks. The asymmetries of oil supply-driven shock on stock returns of the renewable energy sub-group companies are found in most quantiles.

First, this is a company-level study of the stock returns response to the structural oil shocks in the renewable energy sector. Second, to the best of the authors’ knowledge, this type of study is the first in the Indian context. Third using panel quantile regression model along with capital asset pricing model framework, the authors investigate these effects across market conditions.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

This paper aims to explore the determinants of the level of minibonds issued by companies. In doing so, it discusses the importance of minibonds in providing a market-based funding source. In the empirical analysis, special attention is paid to the study of the recovery from the COVID-19 crisis.

The analysis is carried out through an econometric approach, on the basis of a high-quality empirical dataset related to the Italian small- and medium-sized enterprises (SMEs). The reference period covers the recent pandemic. From a theoretical point of view, a regression model is implemented, including a multicollinearity analysis and an outlier detection procedure.

The results of the study indicate that factors such as leverage, cash flow, firm collaterals and seniority can explain the amount of minibonds issued. These findings provide valuable insights into the drivers of minibond issuance and highlight the potential benefits of minibonds as a funding option for Italian SMEs.

Importantly, results highlight relevant managerial implications at two levels. On one side, we carry on a managerial discussion about the worthiness of accessing the minibonds market; on the other side, we give insights on the managerial implications related to the features of the companies issuing minibonds.

The paper investigates an innovative financial instrument that has been introduced recently and has not yet been studied in depth. To the best of our knowledge, this is the first contribution assessing the main drivers for minibonds issuance level, which is a timely and relevant managerial research topic. In addition, this study also takes into account the impact of the COVID-19 pandemic on minibond issuance, making the analysis appropriate for explaining the current economic context.

The purpose of this paper is to demonstrate how network theory and methods can provide insights into the forces shaping architectural learning agendas and knowledge construction in architectural schools.

The methodology involves conceptualising learning as a constructivist process and the agenda as an interconnected network of actors, concepts and relations. Network analysis techniques, including centrality and brokerage metrics, are used to identify roles and knowledge flows using the data locally collected from Turkish universities as well as from the OpenSyllabus open-source database.

The analysis reveals the enduring influence of early modernists, signalling imbalanced canon formation in the architectural learning system. However, marginal voices highlight struggles in integrating unconventional perspectives. Limited integration of local figures indicates a consolidation of Eurocentric epistemes. Identifying these hidden forces is vital for reimagining learning agendas and socio-culturally engaged forms of learning. Pioneering figures demonstrate potential for synthesis when situated as brokers, not bifurcated schools.

The outcomes are limited by the geographical and temporal boundaries of the data and the analysis method employed. Despite limitations, the diagnostic network framework reveals architectural learning as an open, contested ecosystem demanding pluralistic pedagogies concerning not only the global but the local, both canonical and marginal. Further research covering more data could enrich the understanding of qualitative complexities.

The network perspective prompts critical reflexivity about power, ideology and exclusion in knowledge construction. Strategic inclusion and diversification of voices provide pathways to bridge divides and ground learning locally.

This research offers a methodology model to examine forces and influences shaping architectural education by elucidating hidden and remote roles and knowledge gaps in learning agendas. Extending the techniques more widely can enable strategic interventions toward inclusive, impactful learning across disciplines, time and geographies.

This study aims to use geographical information on social media for public opinion risk identification during a crisis.

This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs.

In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced.

Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.