Environmental and cultural erosion caused by overtourism challenges the long-term sustainability of tourist destinations. Scholars present ideological critiques of overconsumption and describe anti-consumption practices, but limited research examines the psychological mechanisms that promote anti-consumption behaviors while traveling. This study adopts Goal Framing Theory to examine a value-lifestyle-behavior framework using a mixed-method research design. The survey data contained 334 respondents and were analyzed using partial least squares structural equation modeling. The results show that anti-consumption values (such as high self-control, an intrinsic source of happiness, and a broad scope of concern) positively influence the voluntary simplicity lifestyle, which encourages anti-consumption behaviors (reduction, rejection, and avoidance). Eco-emotions such as eco-anger, eco-anxiety, and eco-depression strengthen the connection between values and voluntary simplicity. These findings are enhanced by the post hoc qualitative analysis, which identifies contextual triggers (convenience, awareness, social norms, and emotional engagement) that foster travel anti-consumption. This study extends Goal Framing Theory by integrating a value-lifestyle-behavior pathway and establishing eco-emotions as an affective moderator. Managers should engage the growing segment of values-driven travelers by encouraging emotionally laden, lifestyle-compatible, and low-impact tourism offerings.
PurposeThe present study aims to examine how organisations adopt Generative AI and what are the key capabilities that influence the adoption of this technology. The study also investigates how Generative AI adoption influence market performance and sustainability performance. It additionally examines the moderating effect of regulatory support and organizational culture in influencing the association between Generative AI and firm performance.Design/methodology/approachThe study uses a mixed-methods design involving qualitative and quantitative data collection and analysis. The first stage begins with qualitative interviews followed by thematic analysis to establish leading capabilities behind Gen AI adoption, in the second stage, the data were collected from 385 respondents from different organizations which was then analysed using PLS-SEM structural equation modelling.FindingsThe results observed that a firm's digital transformation, innovation and marketing capabilities (MC) significantly enhance its Generative AI Adoption, which further influences firm performance. In addition, regulatory support emerges as a key moderator in driving DTC.Research limitations/implicationsThe findings emphasize that the firm should enhancing digital transformation capabilities, innovation capabilities and MC which can further strengthen the adoption of Generative AI and affect the firm market and sustainability performance. Whereas strengthening regulatory support can enhance the positive impact of DTC and Gen AI on firm sustainability performance.Originality/valueThe study contributes to the literature on Generative AI by shaping an understanding of how the adoption of GenAI relates with the capability of firms in impacting sustainability performance. The research reports a critical gap in the literature by moving beyond the GenAI enthusiasm and assesses whether the advantages of adopting GenAI are durable as the technology diffuses across industries. The study contributes to literature by highlighting the role of regulatory support and determines that how environmental and firm-level factors jointly shape the effective and responsible capitalization of Generative AI for value creation over time.
PurposeThis study aims to examine the collective impact of physical and digital (phygital) marketing strategies on transforming customer engagement and satisfaction in the Indian tourism sector. The need to integrate physical and digital experiences has become increasingly crucial to meet the growing demands of tech-savvy travellers.Design/methodology/approachThis research used a quantitative approach, analyzing data collected from 350 respondents using purposive and snowball sampling. The study examines the impact of phygital marketing strategies, including virtual reality (VR) and augmented reality (AR), on customer engagement and satisfaction using PLS-SEM.FindingsThe results highlighted a significant positive relationship between phygital marketing, customer engagement, and customer satisfaction. The result also demonstrated a significant mediating effect of engagement on satisfaction in phygital marketing. AR/VR technologies, combined with customized, data-driven marketing strategies, lead to higher satisfaction and loyalty among travelers.Research limitations/implicationsTo develop the tourism sector, marketing strategies play a significant role, and there is now a need to integrate personalized content, interactive tools, and immersive technologies. To meet the needs of digital natives, tourism professionals must prioritize implementing data-driven insights and leveraging social media-driven engagement.Originality/valueThis paper integrates service-dominant logic and experiential marketing theory to empirically explain how phygital methods can recreate value in the tourism sector. Marketing phygital: Transformando el engagement y la satisfacci & oacute;n de los nativos digitales en la IndiaObjetivoEste estudio examina el impacto conjunto de las estrategias de marketing f & iacute;sico y digital (phygital) en la transformaci & oacute;n del engagement y la satisfacci & oacute;n del cliente en el sector tur & iacute;stico en la India. La necesidad de integrar experiencias f & iacute;sicas y digitales se ha vuelto cada vez m & aacute;s crucial para satisfacer las crecientes demandas de los viajeros tecnol & oacute;gicamente avanzados.Dise & ntilde;o de la investigaci & oacute;nLa investigaci & oacute;n adopt & oacute; un enfoque cuantitativo, analizando datos recopilados de 350 encuestados mediante muestreo intencional y bola de nieve. El estudio analiza el impacto de las estrategias de marketing phygital, incluidas la realidad virtual (VR) y la realidad aumentada (AR), sobre el engagement y la satisfacci & oacute;n del cliente utilizando PLS-SEM.ResultadosLos resultados evidencian una relaci & oacute;n positiva y significativa entre el marketing phygital, el engagement del cliente y la satisfacci & oacute;n del cliente. Asimismo, se demuestra un efecto mediador significativo del engagement sobre la satisfacci & oacute;n en el contexto del marketing phygital. Las tecnolog & iacute;as AR/VR, combinadas con estrategias de marketing personalizadas y basadas en datos, generan mayores niveles de satisfacci & oacute;n y lealtad entre los viajeros.Implicaciones pr & Atilde;cticasPara el desarrollo del sector tur & iacute;stico, las estrategias de marketing desempe & ntilde;an un art & Atilde;culo fundamental, siendo necesario integrar contenidos personalizados, herramientas interactivas y tecnolog & iacute;as inmersivas. Para satisfacer las necesidades de los nativos digitales, los profesionales del turismo deben priorizar la implementaci & oacute;n de insights basados en datos y el aprovechamiento del engagement impulsado por las redes sociales.Originalidad/valorEl art & iacute;culo integra la l & oacute;gica dominante del servicio (SDL) y la teor & iacute;a del marketing experiencial (EMT) para explicar emp & iacute;ricamente c & oacute;mo los m & eacute;todos phygital pueden crear valor en el sector tur & iacute;stico. Phygital (sic)(sic):(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(phygital)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)350(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic) PLS-SEM (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(VR)(sic)(sic)(sic)(sic)(sic)(AR)(sic)(sic)(sic) phygital (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), phygital (sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic) phygital (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).AR/VR (sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(SDL)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(EMT), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) phygital (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).
Background: There is growing interest in improving supply chain management (SCM) using artificial intelligence and large language models (LLMs). However, the use of LLMs or generative AI presents inherent challenges. Therefore, recognizing these challenges within organizations and understanding how they are interrelated is crucial. Although there is an emerging focus on the use of LLMs in SCM, there remains limited peer-reviewed research exploring the challenges associated with their use in the field. Hence, this study aims to identify the challenges of using LLMs in SCM and to examine the interrelationships among these challenges. Materials and methods: The challenges identified in the literature were validated through the opinions of sixteen experts, including supply professionals, AI specialists, and academics. The study employed Interpretive Structural Modeling (ISM) and Matrice d'Impacts Crois & eacute;s Multiplication Appliqu & eacute;e & agrave; un Classement (MICMAC) analysis to develop a framework consisting of autonomous, driving, linkage, and dependent challenges. Results: The findings show that "Multiple data points in SCM network (C6)" emerges as the key challenge with the highest driving power, whereas "Cost of better optimization (C13)" and "Managerial suspicion toward adopting real-time decision making based on LLM outputs (C14)" are the key dependent challenges. The study also highlights non-technical aspects of these challenges, such as trust and legal considerations, and emphasizes technology adoption from a human and managerial perspective. Conclusions: These findings offer valuable insights into the ranking of challenges as well as their driving and dependent relationships. They can help SCM practitioners and LLM developers address these challenges and facilitate the effective adoption of LLMs in SCM.
Carbon accounting is the monitoring and recording of greenhouse gas (GHG) emissions to mitigate and manage carbon emissions. There are numerous singular studies on carbon accounting across geographies and industries. However, there is a need for a comprehensive study discussing carbon accounting enablers, barriers, policy, and reporting landscape and strategies. This study applies a mixed-method approach to present insights into its enablers, barriers, policy, and reporting landscape and strategies with the help of two integrated studies in this paper. Study A systematically reviews the contemporary literature to identify the thematic focus of carbon accounting literature. The systematic literature review (SLR) conducted on a sample of 53 shortlisted studies comprehends carbon accounting enablers, barriers, policies, and reporting aspects, along with presenting the carbon accounting strategies to reduce and mitigate carbon emissions. The Study B incorporates an empirical analysis of the qualitative responses gathered through essay-based questions designed to list potential carbon accounting strategies articulated by industry experts. This study offers theoretical, practical, and policy implications for all the stakeholders: firm-level managers; city, regional, or national level officers; accounting professionals; investors; sustainable finance providers; and carbon policymakers.
