Search Results (223)

Digital transformation (DT) has become a critical focus in both academia and industry. However, its rapid evolution complicates our understanding of its core concepts and developmental patterns. Understanding the development path of DT is crucial for both scholars and practitioners because it provides a structured view of how the field has progressed over time. This study employs main path analysis (MPA), a citation-based scientometric method, to systematically review and trace the intellectual trajectory of DT research over the past 30 years. Drawing on 1790 academic articles from the Web of Science database, the study identifies key influential works and maps the primary citation paths that shape the field. The analysis reveals three major developmental phases of DT research—engagement, enablement, and enhancement—each characterized by distinct thematic and conceptual shifts. Furthermore, five emerging research trends are uncovered: reinventing digital innovation affordance, value-creation paths of DT, synergistic DT with business and management practices, disciplinary boundaries of DT, and digital leadership. Understanding the intellectual trajectory and emerging trends of DT helps practitioners anticipate technological shifts and align transformation efforts, guiding decision-makers in effectively managing their DT processes. Also, these findings provide a structured framework for understanding the evolution of DT and offer valuable directions for future research in information systems and digital innovation. Full article

This study conducts a comparative assessment of the environmental, social, and governance (ESG) integration strategies of three leading companies in Hong Kong—H&M Group, China Gas Company Limited (Towngas), and MTR Corporation Limited (MTR)—each operating in distinct sectors with unique sustainability challenges and opportunities. The analysis adopts the Challenge–Evaluation–Planning–Action–Review (CEPAR^®) framework developed by the International Chamber of Sustainable Development to examine how these companies identify and evaluate ESG-related risks, formulate action plans, implement sustainability initiatives, and refine their strategies. The findings reveal H&M’s strong emphasis on sustainable fashion, with a target of using 100% sustainable materials by 2030 and reducing greenhouse gas emissions by 56%. Towngas faces the complex challenge of transitioning from fossil fuels to cleaner energy and is investing in zero-carbon technologies to meet regulatory standards and stakeholder expectations. MTR focuses on sustainable urban development and efficient mass transit, prioritizing community engagement and reducing environmental impact. This study underscores the importance of sector-specific ESG approaches tailored to a company’s operational context. It also demonstrates how ESG integration is enhanced by proactive planning, transparent reporting, and alignment with long-term corporate values. By showcasing both successful practices and areas requiring further attention, this research contributes to the broader discourse on sustainable business practices in Hong Kong. Moreover, it provides actionable policy implications for government agencies and regulatory bodies. The insights gained can inform strategic decision-making across sectors and support the development of a more sustainable, resilient, and inclusive economy aligned with Hong Kong’s long-term climate and governance goals. Full article

In an increasingly digitalized marketplace, understanding Generation Z’s (Gen Z) online consumer behavior has become a critical priority, particularly in relation to newly launched technological products. Although online consumer behavior has been widely studied, a gap remains in understanding how the location of the e-shop (domestic vs. international) moderates this behavior. Addressing this gap, the present study adopts a quantitative, cross-sectional design with data from 302 Gen Z participants, using a hybrid sampling method that combines convenience and systematic techniques. A structured questionnaire, grounded in 19 well-established behavioral theories, was employed to examine the influence of six key factors, behavioral and attitudinal traits, social and peer influences, marketing impact, online experience, brand perceptions, and Gen Z characteristics, across various stages of the consumer journey. Moderation analysis revealed that e-shop location significantly affects the strength of relationships between these factors and both purchase intention and post-purchase behavior. Notably, Gen Z’s values and marketing responsiveness were found to be more predictive in the context of international e-shops. These findings highlight the importance of marketing strategies that are both locally relevant and globally informed. For businesses, this research offers actionable insights into how digital engagement and brand messaging can be tailored to meet the unique expectations of Gen Z consumers across diverse e-commerce contexts, thereby enhancing consumer satisfaction, loyalty, and brand advocacy. Full article

With the continuous advancement of the Internet and information technologies, the capacity for development and integration of vehicle and cargo resources has been significantly enhanced, driving the rapid emergence of vehicle–cargo matching platforms. Serving as critical intermediaries between vehicle owners and cargo owners, vehicle–cargo matching platforms effectively address key challenges in traditional logistics, such as low matching efficiency and information asymmetry. As a result, they significantly improve the intelligence and precision of logistics resource allocation. However, at the current stage, vehicle–cargo matching platforms rarely promote freight pooling services, leading to resource underutilization. Due to the freight pooling matching problem involving the combination and allocation of multiple vehicle owners and cargo owners, which is highly complex, few scholars have conducted research on such issues. The lack of coordinated optimization in matching models may result in inefficiencies, and the limited consideration of individual user preferences can lead to low user satisfaction. Therefore, this paper focuses on the freight pooling matching problem in vehicle–cargo matching platforms. To improve matching efficiency and fully consider user preferences, the theory of stable matching is introduced into the freight pooling matching problem. It defines the concepts of combination preferences and group stability based on combination preferences, establishes a group stable matching model for the freight pooling business of vehicle–cargo matching platforms, and verifies the stability of the model through theoretical proof. Since this model is a mixed-integer linear programming model with relatively few decision variables but a large number of constraints, this paper introduces the cutting-plane algorithm. Based on the characteristics of the problem, the algorithm is improved by removing ineffective constraints and only using key constraints, significantly reducing computational complexity, optimizing the solving process, and greatly improving the model’s solution efficiency. This approach aligns well with the characteristics of the vehicle–cargo freight-pooling matching model. The research results indicate that the group stable matching model significantly improves platform revenue, vehicle owners’ profits, and cargo owners’ satisfaction across various supply and demand scenarios. Additionally, the cutting-plane algorithm reduces computation time by 97% and decreases the number of constraints during the solving process by 99%. The stable matching theory and solution algorithm proposed in this paper can provide users with precise matching schemes, significantly improving matching efficiency, user satisfaction, platform revenue and platform competitiveness. It demonstrates high innovation and practical application value. Full article

In the sustainable evolution of the entrepreneurial ecosystem, the efficiency of early-stage capital allocation directly affects the intergenerational transmission capacity of innovation resources. The financing barriers caused by information asymmetry urgently require institutional solutions. This study, based on tracking data from 19,463 startups in China’s information technology sector (2016–2019), analyzes how government-certified incubators (GCIs) optimize the sustainability of the entrepreneurial ecosystem through signaling mechanisms. The empirical results show that collaboration with a GCI can significantly increase the likelihood of IT startups securing venture capital by approximately 25%. This effect is not only due to the strict screening and resource support provided by GCIs, but also due to their role in amplifying internal signals from startups, such as the experience of founders and intellectual property. Notably, in the IT sector, the impact of GCIs is more significant for startups traditionally disadvantaged, particularly those led by female founders. Our research demonstrates that GCIs drive the sustainable development of the entrepreneurial ecosystem through three signaling mechanisms: (1) institutional certification screening, which optimizes the intergenerational allocation efficiency of ecosystem resources; (2) the signaling validation–amplification mechanism, which enhances the value of intellectual property and founder experience, alleviating investors’ challenges in quantifying startup potential; (3) inclusive signal rebalancing, where GCI certification significantly improves the funding success rate of female founders, breaking traditional market biases in screening disadvantaged groups and supporting the inclusive and sustainable development of the entrepreneurial ecosystem. These findings provide a new pathway for emerging economies to optimize the resilience of their entrepreneurial ecosystems through policy tools: for governments, GCIs achieve sustainable development goals at low institutional cost; for investors, the signal integration mechanism reduces investment information friction; and for entrepreneurs, certification endorsements accelerate market validation of sustainable business models. Full article

During the COVID-19 pandemic, the tourism sector encountered multiple challenges. Numerous governments chose to lock down their cities and countries. Despite this, many companies found their online businesses making the greatest leaps in their portfolios, and social media platforms became one of the most valuable sources of information for purchase decisions. There have been numerous studies on the effects of social media reviews—a form of electronic word-of-mouth (eWOM)—on consumer behavior. Few were found to be related to their impact on group package tours (GPTs) while considering mixed eWOM, that is, both the positive and negative forms present in word-of-mouth communication. As the tourism sector gradually revives, the need to further explore how tourism and hospitality service providers can adapt to changes in post-pandemic consumer behavior has become imperative. The influence of social media reviews on consumers’ value perceptions of a GPT to Japan, allowing for the influence of the marketing mix element of advertised price, was examined through online experiments in this study. Positive, negative, and mixed eWOM were examined. It was found that eWOM was more influential on consumers’ value perceptions than the advertised price for all price acceptability levels. Mixed eWOM was found to negatively affect consumers’ final price perceptions which override the impact of quality perceptions in value formations. The value perceptions of the GPT became less acceptable when eWOM was mixed compared to when eWOM was absent or was positive. Mixed eWOM had a negative effect on value perceptions but not as great as when negative eWOM was present, and this was consistently found to apply for all price acceptability levels of the GPT. This study’s contribution to eWOM research and implications for the post-pandemic recovery of tourism and hospitality service providers are made, together with suggested strategies using innovative technologies and communications to enhance their adaptive resilience in the new normal. Full article

Digital supply chains (DSCs) are value-driven and collaborative digital systems designed to generate business value for firms through various innovative technologies. Today, we are witnessing companies transitioning from traditional supply chain models to DSCs through digital technologies. The effective selection of digital suppliers during these digital transformation processes is a strategic research topic. Additionally, factors such as the proliferation of information and communication technologies, globalization, and the pandemic have contributed to the expansion of e-commerce platforms. In this rapid growth phase, identifying the right supplier is crucial for the success of e-commerce sites. This study aims to develop an innovative, integrated, and comprehensive decision-making methodology to assist e-commerce platforms in selecting appropriate suppliers for their DSCs. To achieve this, an extended fuzzy VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method is tested, where criteria and alternative evaluations made by decision-makers (DMs) are characterized by interval-valued intuitionistic fuzzy numbers (IVIFNs). The proposed decision mechanism is tested on the DSS problem of an e-commerce platform specializing in household products. Findings of the application, which uses three experts’ opinion to evaluate four digital suppliers based on the seven criteria, are discussed to help e-commerce sites conduct the DSS process more effectively. Full article

Academic publications from the Web of Science Core Collection on “e-business” and “artificial intelligence” (AI) are investigated to reveal the role of AI, extract latent themes and identify potential research topics. The proposed methodology includes relevant graphical representations (trends, co-occurrence networks, Sankey diagrams), sentiment analyses and latent topics identification. A renewed interest in these publications is evident post-2018, with a sharp increase in publications around 2020 that can be attributed to the COVID-19 pandemic. Chinese institutions dominate the collaboration network in e-business and AI. Keywords such as “business transformation”, “business value” and “e-business strategy” are prominent, contributing significantly to areas like “Operations Research & Management Science”. Additionally, the keyword “e-agribusiness” recently appears connected to “Environmental Sciences & Ecology”, indicating the application of e-business principles in sustainable practices. Although three sentiment analysis methods broadly agree on key trends, such as the rise in positive sentiment over time and the dominance of neutral sentiment, they differ in detail and focus. Custom analysis reveals more pronounced fluctuations, whereas VADER and TextBlob present steadier and more subdued patterns. Four well-balanced topics are identified with a coherence score of 0.66 using Latent Dirichlet Allocation, which is a probabilistic generative model designed to uncover hidden topics in large text corpora: Topic 1 (29.8%) highlights data-driven decision-making in e-business, focusing on AI, information sharing and technology-enabled business processes. Topic 2 (28.1%) explores AI and Machine Learning (ML) in web-based business, emphasizing customer service, innovation and workflow optimization. Topic 3 (23.6%) focuses on analytical methods for decision-making, using data modeling to enhance strategies, processes and sustainability. Topic 4 (18.5%) examines the semantic web, leveraging ontologies and knowledge systems to improve intelligent systems and web platforms. New pathways such as voice assistance, augmented reality and dynamic marketplaces could further enhance e-business strategies. Full article

Data-driven technologies in smart buildings offer significant opportunities to enhance energy efficiency, sustainability, and occupant comfort. However, the existing literature often lacks a holistic examination of the technological advancements, adoption barriers, and business models necessary to realize these benefits. To address this gap, this scoping review synthesizes current research on these technologies, identifies factors influencing their adoption, and examines supporting business models. Inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a structured search of the literature across four major databases yielded 112 relevant studies. The key technologies identified included big data analytics, Artificial Intelligence, Machine Learning, the Internet of Things, Wireless Sensor Networks, Edge and Cloud Computing, Blockchain, Digital Twins, and Geographic Information Systems. Energy optimization is further achieved through integrating renewable energy resources and advanced energy management systems, such as Home Energy Management Systems and Building Energy Management Systems. Factors influencing adoption are categorized into social influences, individual perceptions, cost considerations, security and privacy concerns, and data quality issues. The analysis of business models emphasizes the need to align technological innovations with market needs, focusing on value propositions like cost savings and efficiency improvements. Despite the benefits, challenges such as high initial costs, technical complexities, security risks, and user acceptance hinder their widespread adoption. This review highlights the importance of addressing these challenges through the development of cost-effective, interoperable, secure, and user-centric solutions, offering a roadmap for future research and industry applications. Full article

Companies have tried to innovate in their training processes to increase their productivity indicators, reduce equipment maintenance costs, and improve the work environment. The use of Augmented Reality (AR) has been one of the implemented strategies to upgrade training processes, since it optimizes, through User Interface (UI) Design, experiences designed for users (UX) that are focused on education and training contexts. This research describes the definition and implementation of an IT architecture based on the ISO/IEC/IEEE 42010 standard using the Zachman and Kruchten frameworks. The methodological proposal presents an architecture seen from a business perspective, taking into account the strategic and technological components of the organization under a strategic alignment approach. The result is a six-layer architecture: The Government Strategy Layer (1) that accounts for the strategic component; the Business Layer (2) that presents the business management perspective; the Information Layer (4) that defines the metrics system: efficiency through task time, effectiveness through tasks completed, and satisfaction with overall satisfaction. In the Data Layer (4), the data collected with the metrics are structured in an industrial scenario with a cylinder turning process on a Winston Lathe. The experiment was carried out with two groups of 272 participants. In the Systems and Applications Layer (5), two applications were designed: a web client and a mobile application with augmented reality, and finally, the Networks and Infrastructure Layer (6), which delivers the two functional applications. The architecture validation was carried out using the mobile application. The analysis of the results showed a significance value of less than 0.001 in the three indicators: efficiency, effectiveness, and satisfaction in the Levene test and Student’s t-test. To corroborate the results, a test of equality of means with the Mann–Whitney U was carried out, showing that the three indicators presented significantly different values in the two experimental groups of this study. Thus, the group trained with the application obtained better results in the three indicators. The proposed architecture is adaptable to other training contexts. Information, data, and systems and application layers allowed for the exchange of training processes so that the augmented reality application is updated according to the new requirements. Full article

The relationship between AI and management decision-making has received increasing attention in the literature, but the impact of AI on managerial decision-making through transformational leadership has not yet been thoroughly examined. Thus, this study investigates the impact of artificial intelligence on engineering management decision-making through transformational leadership. The participants include 385 employees drawn from manufacturing, construction, and information technology firms in Turkey. The data were processed using WarpPLS (7.0), and the estimation was conducted with the use of “partial least squares structural equation modeling (PLS-SEM)”. A positive and significant direct influence of “artificial intelligence” and “transformational leadership” on engineering management decision-making practices was demonstrated in this study, while transformational leadership was also found to have a significant mediating role in the relationship between artificial intelligence and engineering management decision-making practices. This study concluded with theoretical and practical implications for policymakers in the engineering industry by providing an integrated framework that allows for a nuanced examination of how AI impacts engineering management decision-making. It accounts for individual perceptions, leadership influences, and organizational adaptations, providing a comprehensive lens through which to analyze the complex interplay between AI technology, leadership, and decision-making processes in engineering management contexts. In addition, the findings of our study have significant implications for engineers and for governments creating standards to help preserve engineering businesses. Leaders and practitioners should research the instillation of values inherent to AI for an organization like engineering businesses to ensure that AI is being used to enable effective decision-making towards ensuring the accomplishment of their sustainable competitive advantage. Full article

Information is an experience good, meaning that its value emerges upon use and varies based on individual perceptions. Augmented reality (AR) is a technology designed to deliver immersive informational experiences. This study investigates the impact of AR on the perceived value of information by people experiencing information as consumers or producers. Using Google Maps, we developed an AR mobile map called Tour-It-Yourself (TIY) for city navigation. The TIY app facilitates content consumption and production around local points of interest engagingly and interactively. We discuss the development of the research tool and results from two user studies (N = 37 teenagers and N = 51 students, respectively). Further, we highlight the economic valuation aspect by examining the effects of AR on Willingness-to-Pay and Willingness-to-Accept payment for information. The results indicate a significant main effect of participant experience (consumer/producer) and AR technology on the perceived evaluation of information. Moreover, the addition of AR technology to information improves knowledge regarding points of interest for producers. These findings contribute to our understanding of how AR shapes information value perception and have implications for designing interactive location-based information systems, benefiting tourism, education, and business, as well as fostering a sense of belonging in cities. Full article

This study focuses on two significant factors shaping university students’ perception of environmental sustainability. Those are (a) personal values, measured by the Portrait Values Questionnaire (PVQ) and (b) the usage of and proficiency in information technology. Personal values have been widely used to analyze individual perspectives toward various issues, including environmental ones. As contemporary social arrangements include significant influences from social networks and mobile phone usage, overall engagement with technology becomes an essential factor affecting university student attitudes and behaviors. While the individual impact of those factors can be found in the extant literature, we look into their interaction concerning university student pro-environmental attitudes, measured by the Revised NEP (New Ecological Paradigm) Scale, and their environmental self-efficacy. Our sample comes from the population of undergraduate business and economics students from Central and Southeast Europe, specifically Slovenia, Croatia, Bosnia and Herzegovina, Serbia, and Montenegro. We discuss our findings in the context of previous studies from the same regions and consider the unique socio-economic factors. The research results address Sustainable Development Goals 4 (quality education), 12 (responsible consumption and production), and 13 (climate action) by analyzing the factors contributing to university student pro-environmental attitudes and their self-efficacy in the context of their information technology usage and proficiency. Full article

The AEC industry, including construction consulting, is experiencing digitalization through technologies like Building Information Modeling (BIM), impacting firms’ business models. The traditional hourly-based model does not align with shortened project timelines driven by digitalization, design automation, and product-based approaches. This study uses a case study approach and explores and evaluates four selected alternative revenue models (freemium, subscription, out-come-based, and performance-based) for value creation using a digital tool developed by the consulting firm. The freemium model enhances market reach but requires a high conversion rate. The subscription model offers steady recurring revenue and supports client relationships. Out-come-based and performance-based models link payments to client outcomes and performance metrics, fostering efficiency and competitive advantages. The study identified multiple target markets for this tool and emphasized the importance of a portfolio revenue model to meet specific client needs in the digital era. Implementing alternative revenue models aids return on investments and avoids the digitalization paradox. Construction consulting firms must adeptly manage these models’ complexities to address diverse client needs and leverage digital transformation opportunities. Full article

As businesses increasingly adopt digital processes and solutions to enhance efficiency and productivity, they face heightened cybersecurity threats. Through a systematic literature review and concept development, this article examines the intersection of digitalisation and cybersecurity. It identifies the methodologies and tools used for cybersecurity assessments, factors influencing the adoption of cybersecurity measures, and the critical success factors for implementing these measures. The article also puts forward the concept of cybersecurity governance process categories, which are used to classify the factors uncovered in the research. Findings suggest that current information security standards tend to be too broad and not adequately tailored to the specific needs of small and medium-sized enterprises (SMEs) when implementing emerging technologies, like Internet of Things (IoT), blockchain, and artificial intelligence (AI). Additionally, these standards often employ a top-down approach, which makes it challenging for SMEs to effectively implement them, as they require more scalable solutions tailored to their specific risks and limited resources. The study thus proposes a new framework based on the Plan-Do-Check model, built around the cybersecurity governance process categories and the three core pillars of governance, culture and standards. This is essentially a bottom-up approach that complements current top-down methods, and will be of value to both information technology (IT) professionals as an operational guide, and to researchers as a basis for future research in this field. Full article