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Consumers may enjoy the information sharing and social support made available when a social media platform is used for pre-purchase research; however, do consumers reevaluate the privacy and security of the platform differently when ordering and payment capabilities are added? As social media systems have evolved into social commerce platforms (SCPs), individuals are often faced with whether to complete a purchase they have been researching or switch to a traditional e-commerce platform to complete the transaction. This research examines consumer trust formation in the SCP channel and how consumer interest and engagement in the channel are maintained and influence consumer decisions to purchase via the SCP.

Based on trust and involvement literature, a research model was conceptualized to capture consumer beliefs about SCP privacy and security and whether the SCP can be trusted, using these inputs into subsequent consumer interest, engagement and decisions on whether to use the SCP for purchasing. The research model was empirically tested using the panel data's structural equation modeling (AMOS) (n = 405). The data showed acceptable reliability and convergent validity, while the original research model provides predictive validity and theory-confirming insights.

Results confirm that consumer perceptions of privacy and security play a crucial role as decision criteria, informing their judgments of whether a new social commerce channel can be trusted enough to conduct purchases. Further, consumer trust supports their interest in the SCP, resulting in enduring and enhanced behavioral use and, to a lesser extent, purchase intent. Still, a majority of this sample declined to purchase using the SCP and rather preferred to transact on tried and trusted traditional e-commerce sites.

This study is among the first to examine trust formation in new SCPs, where consumers are deciding to expand their engagement level from social and informational to commercial.

This paper aims to unravel the relationship between market-driven impacts of climate change and firms’ deployment of renewable energy (RE) innovation. The purpose is to understand how market-related forces, influenced by uncertainty, shape firms’ behaviour in response to climate change challenges.

Drawing on the behavioural theory of the firm (BTOF), the paper develops a conceptual model to decode the relationship between each category of market-driven impacts and the resulting RE innovation within firms. The model takes into account the role of uncertainty and differentiates between multinational enterprises (MNEs) and domestic firms.

The analysis reveals five key sources of market-driven impacts: investor sentiment, media coverage, competitors’ adoption of ISO 14001, customer satisfaction and shareholder activism. These forces influence the adoption of RE innovation differently across firms, depending on the level of uncertainty and the discrepancy between environmental performance and aspiration level.

This paper contributes to the literature in four ways. Firstly, it emphasises the importance of uncertainty associated with market-driven impacts, which stimulates different responses from firms. Secondly, it fills a research gap by focusing on the proactivity of firms in adopting RE innovation, rather than just operational strategies to curb emissions. Thirdly, the paper extends the BTOF by incorporating the concept of uncertainty in explaining firm behaviour. Finally, it provides insights into the green strategies of MNEs in the face of climate change, offering a comprehensive model that differentiates MNEs from domestic firms.

This study aims to apply the service robot acceptance model (sRAM) to examine how attitude toward human–robot interaction (HRI) and engagement influence consumer acceptance of service robots in a frontline setting.

Data was collected from 255 visitors who interacted with a robotic tour guide at a city museum. The data was analyzed using smart PLS 4.0.

The findings show the positive effects of subjective norms, appearance, perceived trust and positive emotion on both attitude toward HRI and engagement. In addition, social capability impacted attitude toward HRI, whereas perceived usefulness affected engagement.

To deliver engaging museum experiences that bring about positive word-of-mouth and intention to visit, managers need to incorporate the sRAM dimensions in the design and deployment of service robots.

This research uses field data to empirically validate the sRAM in the context of service robot acceptance. It introduces engagement as a novel mediating variable, enriching current understanding of human-like qualities in HRIs.

Major public cloud providers, such as AWS, Azure or Google, offer seamless experiences for infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). With the emergence of the public cloud's vast usage, administrators must be able to have a reliable method to provide the seamless experience that a public cloud offers on a smaller scale, such as a private cloud. When a smaller deployment or a private cloud is needed, OpenStack can meet the goals without increasing cost or sacrificing data control.

To demonstrate these enablement goals of resiliency and elasticity in IaaS and PaaS, the authors design a private distributed system cloud platform using OpenStack and its core services of Nova, Swift, Cinder, Neutron, Keystone, Horizon and Glance on a five-node deployment.

Through the demonstration of dynamically adding an IaaS node, pushing the deployment to its physical and logical limits, and eventually crashing the deployment, this paper shows how the PackStack utility facilitates the provisioning of an elastic and resilient OpenStack-based IaaS platform that can be used in production if the deployment is kept within designated boundaries.

The authors adopt the multinode-capable PackStack utility in favor of an all-in-one OpenStack build for a true demonstration of resiliency, elasticity and scalability in a small-scale IaaS. An all-in-one deployment is generally used for proof-of-concept deployments and is not easily scaled in production across multiple nodes. The authors demonstrate that combining PackStack with the multi-node design is suitable for smaller-scale production IaaS and PaaS deployments.

Professional Identity Formation is the dynamic evolution to “think, act and feel” to become part of a professional community. This document presents the development and the study that aimed to assess the usability of a m-Learning Identity App (MLIA) focused on the formation of professional identity among undergraduate medical students.

MLIA development included four phases: Conceptual, prototype, pilot and implementation, before further deployment. The conceptual model was designed by eight faculty members from three Latin American universities. The prototype was developed and tested with stakeholders. The pilot was performed during 5 weeks before the implementation. Cross-sectional data collected during implementation from 138 medical students who completed a survey to assess the usability of MLIA are presented. During deployment, 977 posts were made on Professional Identity Formation, and examples of these posts are presented.

The prototype and pilot phases demanded improvements. The survey explored (1) Familiarity, (2) Perceived ease of use, (3) Perceived usefulness for Professional Identity Formation, (4) Satisfaction, (5) Intention to reuse (6) Digital aesthetics and (7) Safety. Results from the usability assessment suggest that students perceived MLIA as a secure space with positive aesthetics and ease of use.

Important limitations of the present study include, firstly, that it does not provide information on the effectiveness of the MLIA in shaping professional identity in medical students, it focuses exclusively on its development (conceptual model, prototype, pilot and implementation) and usability. Secondly, the study design did not consider a control group and, therefore, does not provide information on how the App compares with other strategies addressing self-reflection and sharing of meaningful experiences related to professional identity.

MLIA introduces a different approach to education, simulating a secure, easy-to-use, social media with a friendly interface in a safe environment to share academic and motivational moments, transitioning from being to becoming a professional.

The purpose of this study is to investigate the impact of corporate social responsibility (CSR) on innovation performance and examine the moderating role of social media strategic capability and big data analytics capability. Specifically, the authors explore the effects of both external and internal CSR on innovation performance.

The authors collected data from 221 senior, middle and research and development (R&D) managers of high-tech firms in China, using a questionnaire survey with a six-month interval.

The empirical results show that both external and internal CSR positively influence innovation performance. Furthermore, social media strategic capability has a positive moderating effect on the relationship between CSR and innovation performance, while big data analytics capability moderates the relationship between external CSR and innovation performance.

The data comes from high-tech firms in China, which may limit the generalizability and external validity of the findings. Future studies should replicate this study in other industries and types of organizations.

The study suggests that high-tech firms should engage in both external and internal CSR activities to promote innovation performance. Moreover, leveraging social media strategic capability and big data analytics capability can enhance innovation performance.

This study contributes to the literature on CSR outcomes by empirically exploring the effects of external and internal CSR on innovation performance, thus extending stakeholder theory. Additionally, by revealing the contingency effects of social media strategic capability and big data analytics capability, this study enriching the research on dynamic capabilities theory in the context of digital transformation.

The authors proposed a conceptual model by examining the influence of threats to their freedom on tourists’ psychological distance including social distance, spatial distance, and temporal distance, which effect psychological reactance and the consequent online Airbnb booking intentions. Furthermore, media intrusiveness as a moderator determines the boundary conditions between perceived threats to their freedom and social distance, spatial distance, and temporal distance.

Data was gathered from 491 Chinese travelers to provide empirical evidence. The authors performed data analysis in Amos 26.0 using structural equation modeling (SEM) and Hayes (2013) PROCESS macro.

The findings positively reinforced all the structural relationships of the study. Notably, media intrusiveness significantly moderates the association between perceived threats to their freedom and psychological distance (i.e. social distance, spatial distance, and temporal distance).

The findings contribute significantly to the field of social psychology, advertising, and consumer behavior derive prolific implications for policymakers and sharing economy platforms. Lastly, by identifying limitations, this research opens doors for future scholars.

Governments' acute precautionary measures in response to the COVID-19 outbreak have confined individual freedom across the globe. This study illuminates how tourists conceive these preventative measures as perceived threats to their freedom, and subsequently engage psychological reactance.

In this study, the deployment of an integrated Quality Function Deployment (QFD) decision framework is presented to help cities design targeted strategies to become “smart”. Arguably smart cities leverage advanced technologies to enhance their smartness to improve everyday urban life. To this end, a QFD – Analytic Hierarchy Process – Analytic Network Process (QFD-AHP-ANP) framework is proposed to deliver guidance for selecting the appropriate mix of smart technologies based on the specific smart needs of each city.

The AHP and ANP methods are incorporated into QFD to enhance its methodological robustness in formulating the decision problem. AHP accurately captures and translates the “Voice of the Experts” into prioritized “Smart City” dimensions, while establishing inter-relationships between these dimensions and “Smart City Technologies”. Meanwhile, ANP explores tradeoffs among the technologies, enabling well-informed decisions. The framework’s effectiveness is evaluated through an illustrative application in the city of Thessaloniki.

Applying the framework to this real-world context confirms its practicality and utility, demonstrating its ability to particularize local, social, political, environmental and economic trends through the resulting mix of technologies in smart urban development strategies.

The importance of this study lies in several aspects. Firstly, it introduces a novel QFD decision framework tailored for smart city strategic planning. Secondly, it contributes to the operationalization of the smart city concept by providing guidance for cities to effectively adopt smart technologies. Finally, this study represents a new field of application for QFD, expanding its scope beyond its traditional domains.

ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.

The purpose of this study is to examine various data sources to identify trends and themes in humanitarian supply chain management (HSCM) in the digital age.

In this study, various data sources such as published literature and social media content from Twitter, LinkedIn, blogs and forums are used to identify trending topics and themes on HSCM using topic modelling.

The study examined 33 published literature and more than 94,000 documents, including tweets and expert opinions, and identified eight themes related to HSCM in the digital age namely “Digital technology enabled global partnerships”, “Digital tech enabled sustainability”, “Digital tech enabled risk reduction for climate changes and uncertainties”, “Digital tech enabled preparedness, response and resilience”, “Digital tech enabled health system enhancement”, “Digital tech enabled food system enhancement”, “Digital tech enabled ethical process and systems” and “Digital tech enabled humanitarian logistics”. The study also proposed a framework of drivers, processes and impacts for each theme and directions for future research.

Previous research has predominantly relied on published literature to identify emerging themes and trends on a particular topic. This study is unique because it examines the ability of social media sources such as blogs, websites, forums and published literature to reveal evolving patterns and trends in HSCM in the digital age.