Mohit Goswami, M. Ramkumar, Jiju Anthony, Raja Jayaraman, Beth Cudney and Felix T.S. Chan
This study aims to develop analytical models that consider product quality and production volume as essential drivers for profitability in the marketplace. It also considers…
Abstract
Purpose
This study aims to develop analytical models that consider product quality and production volume as essential drivers for profitability in the marketplace. It also considers product demand and price dynamics to understand related nuances backed by empirical validation.
Design/methodology/approach
The pricing mechanism is influenced by production quality, while product demand is influenced by both price and quality. The study considers cost elements, including production cost and quality loss cost which in turn are influenced by production volume and product quality. It establishes analytical conditions for optimal product quality and applies them to numerical analyses considering four distinct industry settings.
Findings
The study reveals that unique solutions exist for optimal product quality at each production level in four industry scenarios. The optimal production volume depends on product quality, and empirical research validates these findings from analytical models and numerical analysis.
Originality/value
This study represents a pioneering effort to investigate operational strategies in both analytical and empirical contexts, thus contributing to the existing body of knowledge in this area.
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Ulrich R. Orth, Caroline Meyer, Jule Timm, Felix Reimers and Tatiana Bouzdine-Chameeva
This study aims to integrate research on multimodal congruency with the stereotype-content model to offer a novel explanation of why and when consumers respond favorably to…
Abstract
Purpose
This study aims to integrate research on multimodal congruency with the stereotype-content model to offer a novel explanation of why and when consumers respond favorably to vision-sound congruency in online service settings.
Design/methodology/approach
A mixed methods approach included a field study (360° panoramic desktop-virtual tour of a winery) and a laboratory study (fully immersive virtual realtiy (VR) tour of a pub). The explanatory mechanism was tested through conditional process analyses, specifically, a custom-made serial mediation model where effects of cross-modal congruency were channeled through telepresence and warmth/competence with familiarity with the service provider included as a moderator. Category knowledge and involvement were included as controls. Study 2 additionally accounted for sensory olfactory and haptic information present in the consumer’s physical location.
Findings
Congruency between vision and sound positively influences consumer intention to visit the environment in person, to purchase online and to engage in positive word-of-mouth. These effects are channeled through enhanced feelings of telepresence as well as more favorable perceptions of service provider warmth. Congruency effects are robust in the presence of additional sensory input in the offline environment and across levels of involvement and knowledge but may depend on a consumer’s familiarity with the setting.
Research limitations/implications
The study offers a novel process explanation for how cross-modal congruency in online service settings influences consumer intention. Examining two specific sensory modalities and two service settings presents limitations.
Practical implications
The findings help service providers to better understand how perceptions of warmth and competence transmit cross-modal congruency effects, resulting in more favorable responses.
Originality/value
To the best of the authors’ knowledge, this work is among the first to adopt a stereotype-content and multimodal congruency perspective on consumer response to online service settings.
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Shivakami Rajan and L.R. Niranjan
This research examines the complex relationship between usage of Chat Generative Pre-Trained Transformer (ChatGPT) amongst student and their creativity, learning and assessment…
Abstract
Purpose
This research examines the complex relationship between usage of Chat Generative Pre-Trained Transformer (ChatGPT) amongst student and their creativity, learning and assessment using empirical data collected from postgraduate students. In addition, the study explores the student’s intrinsic motivation for usage to understand student categories. This research seeks to provide further insights into this artificial intelligence tool in enhancing the educational ecosystem for all stakeholders concerned.
Design/methodology/approach
The target population of this research – the students of post-graduation in diverse fields of science and management. A five-point Likert scale-structured questionnaire adapted from earlier literature relevant to the research questions was adopted for data collection. The data were collected for two months, resulted in 403 usable responses. Ethical considerations of assurance of confidentiality to the participants were strictly adhered to. Structured equation modelling (SEM) was employed to explore the relationships between the constructs of the study for the assessment of latent relationships. SmartPLS 4 was used to explore these relationships.
Findings
Usage has a negative impact on a student’s creativity, but increased usage of ChatGPT encourages a student’s adoption due to its perceived usability. Pedagogical applications of ChatGPT aid students as a learning tool but require controlled usage under supervision.
Originality/value
This study is innovative in the context of postgraduate students, where very little evidence of creativity exists. Through this research, the authors illuminate how ChatGPT use affects academic performance, benefiting educators as a tool but for evaluation and assessment, policymakers and students. The findings of the study provide implications that help to create effective digital education strategies for stakeholders.
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Álvaro Hernández-Tamurejo, María Fernández-Fernández and Paula González-Padilla
This study investigates Generation Z’s perceptions of entrepreneurial innovation through the Metaverse, including user perceptions of privacy and trust, and their impact on the…
Abstract
Purpose
This study investigates Generation Z’s perceptions of entrepreneurial innovation through the Metaverse, including user perceptions of privacy and trust, and their impact on the acceptance of Metaverse technology.
Design/methodology/approach
Using a quantitative approach to examine relationships between the targeted factors, this study employs a structural equation modeling (SEM) based on the technology acceptance model extended with the variables related to privacy, trust and product innovation. The data were collected through a survey distributed to a representative sample of Gen Z individuals.
Findings
The results reveal that user perception of trust and product innovation positively influences attitudes toward using the Metaverse and the behavioral intention to use it. However, while privacy risk is found to significantly influence trust, it does not affect user attitudes or intention to use the Metaverse.
Practical implications
The results of this study provide useful insights for enterprises, raising considerations to maximize the innovative potential of the Metaverse in the current business ecosystem. The understanding of Gen Z’s perceptions can help enterprises to better adapt their innovation management practices so as to effectively engage this demographic, ensuring the successful adoption of Metaverse technologies.
Originality/value
This study is among the first empirical investigations on the impact of Gen Z on innovation management through the Metaverse, which is an emerging and increasingly important area.
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Amer Jazairy, Emil Persson, Mazen Brho, Robin von Haartman and Per Hilletofth
This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into…
Abstract
Purpose
This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.
Design/methodology/approach
Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.
Findings
The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.
Research limitations/implications
This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.
Practical implications
The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.
Originality/value
This is one of the first SLRs on drone applications in LMD from a logistics management perspective.
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Xi Luo, Jun-Hwa Cheah, Xin-Jean Lim, T. Ramayah and Yogesh K. Dwivedi
The increasing popularity of live-streaming commerce has provided a new opportunity for e-retailers to boost sales. This study integrated signaling theory and social exchange…
Abstract
Purpose
The increasing popularity of live-streaming commerce has provided a new opportunity for e-retailers to boost sales. This study integrated signaling theory and social exchange theory to investigate how streamer- and product-centered signals influence customers’ likelihood of making an impulsive purchase in the live-streaming commerce context.
Design/methodology/approach
An online survey was designed and distributed to the target respondents in China using purposive sampling. A total of 735 valid responses were analyzed with partial least square structural equation modeling (PLS-SEM).
Findings
Both streamer-centered signals, i.e. streamer credibility and streamer interaction quality, were discovered to significantly influence product-centered signal, i.e. product information quality. Additionally, streamer interaction quality was found to have a significant impact on streamer credibility. Furthermore, it was observed that customer engagement played a significant mediating role in the relationship between product information quality and impulsive buying tendency. Moreover, the paths between product information quality and customer engagement, as well as the connection between engagement and impulsive buying tendency, were found to be moderated by guanxi orientation.
Originality/value
Despite the prevalence of impulsive purchases in live-streaming commerce, few studies have empirically investigated the impact of streamer and product signals on influencing customers’ impulsive purchase decisions. Consequently, to the best of our knowledge, this study distinguishes itself by offering empirical insights into how streamers use reciprocating relationship mechanisms to communicate signals that facilitate impulsive purchase decisions.
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Mousa Al-kfairy, Obsa Sendaba and Omar Alfandi
This study investigates the impact of social cognitive theory (SCT) constructs and perceived risks on university students’ trusting intentions towards Metaverse-based educational…
Abstract
Purpose
This study investigates the impact of social cognitive theory (SCT) constructs and perceived risks on university students’ trusting intentions towards Metaverse-based educational platforms in the UAE. By examining factors such as self-efficacy, outcome expectations and vicarious learning (from SCT), alongside perceived risks like performance, time, social and security concerns, this research addresses critical gaps in understanding trust dynamics in educational technology.
Design/methodology/approach
A quantitative survey was conducted with 176 university students who experienced a Metaverse-based classroom prototype. Data were analyzed using structural equation modeling (SEM) to evaluate the relationships between SCT constructs, perceived risks and trusting intentions.
Findings
The results demonstrate that SCT constructs significantly enhance trust by fostering self-efficacy and providing positive learning experiences. Conversely, perceived risks reduce trust, emphasizing the need to mitigate security concerns and usability barriers to improve adoption. These insights underline the dual importance of managing risks and promoting psychological readiness among students.
Practical implications
The findings offer actionable guidance for educators, policymakers and developers to design secure, user-friendly Metaverse platforms that align with educational objectives. The study emphasizes the importance of addressing perceived risks, enhancing student engagement and fostering trust to enable effective technology adoption in education.
Originality/value
This research provides a novel perspective on trust in Metaverse-based education by integrating SCT constructs with risk perceptions, offering a comprehensive framework to guide the successful implementation of immersive learning environments.
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Saleh Abu Dabous, Ahmad Alzghoul and Fakhariya Ibrahim
Prediction models are essential tools for transportation agencies to forecast the condition of bridge decks based on available data, and artificial intelligence is paramount for…
Abstract
Purpose
Prediction models are essential tools for transportation agencies to forecast the condition of bridge decks based on available data, and artificial intelligence is paramount for this purpose. This study aims at proposing a bridge deck condition prediction model by assessing various classification and regression algorithms.
Design/methodology/approach
The 2019 National Bridge Inventory database is considered for model development. Eight different feature selection techniques, along with their mean and frequency, are used to identify the critical features influencing deck condition ratings. Thereafter, four regression and four classification algorithms are applied to predict condition ratings based on the selected features, and their performances are evaluated and compared with respect to the mean absolute error (MAE).
Findings
Classification algorithms outperform regression algorithms in predicting deck condition ratings. Due to its minimal MAE (0.369), the random forest classifier with eleven features is recommended as the preferred condition prediction model. The identified dominant features are superstructure condition, age, structural evaluation, substructure condition, inventory rating, maximum span length, deck area, average daily traffic, operating rating, deck width, and the number of spans.
Practical implications
The proposed bridge deck condition prediction model offers a valuable tool for transportation agencies to plan maintenance and resource allocation efficiently, ultimately improving bridge safety and serviceability.
Originality/value
This study provides a detailed framework for applying machine learning in bridge condition prediction that applies to any bridge inventory database. Moreover, it uses a comprehensive dataset encompassing an entire region, broadening the model’s applicability and representation.
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Pedro Dourado, Carmen LLovet and Eglée Ortega Fernández
Given the potential for authorial fashion to lead the way in the field of sustainable fashion and digital platforms to be a powerful tool for spreading sustainable messages, this…
Abstract
Purpose
Given the potential for authorial fashion to lead the way in the field of sustainable fashion and digital platforms to be a powerful tool for spreading sustainable messages, this study seeks to explore the emphasis given to sustainability in the digital communication of Portuguese and Spanish’s authorial fashion.
Design/methodology/approach
A thematic analysis of all the posts published on the feeds of the 63 designers who presented their collections at the Madrid and Lisbon fashion weeks in September/October 2022 (Spring/Summer 2023) or February/March 2023 (Fall/Winter 2023/2024) editions was carried out. The information was collected over a six-month period between the two fashion weeks. This study is based on the categories identified in the reflexive thematic analysis developed by Testa et al. (2020). After adapting the categories to our analysis, 14 emerging themes were identified as observation criteria.
Findings
A total of 4,699 posts were examined. The analysis reveals a general lack of interest in mentioning sustainability – a subject evident in just around 6% of the content – as well as a high emphasis on the visual aspect of the fashion product. Several similarities between the Portuguese and Spanish markets were observed.
Originality/value
This study is important since there are few cross-cultural studies in the field of fashion between the two countries, particularly on sustainable fashion. Furthermore, it establishes a structure that is easily replicable in various markets and geographical areas.
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Vinod Kumar, Sachin Kumar, Ranjan Chaudhuri, Sheshadri Chatterjee, Demetris Vrontis and Saeedeh Rezaee Vessal
This study aims to examine how an organization’s innovation capability could influence research and development (R&D) performance. It also investigates if industry–academic…
Abstract
Purpose
This study aims to examine how an organization’s innovation capability could influence research and development (R&D) performance. It also investigates if industry–academic knowledge transfer has a moderating relationship between organizational innovation capability and exploration and exploitative innovation in improving the R&D performance of the organizations.
Design/methodology/approach
Based on the literature and dynamic capability view, a conceptual model was developed and then validated using the partial least squares-structural equation modeling technique considering 387 responses from academicians and industry personnel.
Findings
The study found that industry–academic knowledge transfer has a significant moderating impact toward improving innovation capability, organizations’ R&D performance and exploration innovation. However, it has an insignificant moderating impact on improving innovation capability and exploitative innovation.
Practical implications
Organizational innovation capability is characterized by both exploratory and exploitative innovation. Both types of innovation support the R&D performance of an organization. Also, organizations that closely work with academic institutions could gain significant R&D knowledge from academic expertise. This study provides food for thought for the academic community as well as industry policymakers.
Originality/value
There are significant opportunities for academic institutions to gain practical knowledge from industry which can help them to accelerate their R&D activities. However, transferring knowledge between industry and academia has challenges related to intellectual property, patents and so on. Not much research has been conducted in this area. Thus, the proposed research model is unique and adds to the existing literature.