PurposeDespite growing research on blockchain value, little is known about the role of CEOs in shaping these outcomes. This study examines whether CEO external directorships influence market reactions to blockchain investment announcements and how adoption strategy and application type condition this relationship through interaction effect.Design/methodology/approachWe conduct an event study of 200 blockchain-related announcements by 138 U.S. public firms and use regression analyses to assess the effects of CEO external directorships, adoption strategy and application type.FindingsCEO external directorships positively affect market responses to blockchain investments, with stronger effects for collaborative initiatives and financial applications. The results highlight the contingent value of CEO external social capital in complex IT investment decisions.Practical implicationsCEO external board ties can be a strategic asset in navigating regulatory, collaborative and technological complexities in blockchain projects. Boards and investors may consider executive network capital when evaluating leadership and strategic IT decisions.Originality/valueThis study offers one of the first pieces of empirical evidence on how CEO external directorships influence market reactions to blockchain investments. More importantly, it extends social capital, signaling and governance theories by showing that in blockchain investment contexts - characterized by heightened inter-organizational coordination, regulatory scrutiny and interpretive uncertainty - CEO external directorships function as context-activated social capital, whose value depends on adoption strategy and application type rather than being universally beneficial.

This study employs a Design Science Research approach to propose a foundational information system design theory tailored for Generative Artificial Intelligence (GenAI) applications in the fashion design process. It delineates meta-requirements and design principles that address both the transformative potential of GenAI, and the unique challenges faced by the fashion sector. To validate the practicality of the proposed design theory, a prototype system was developed and evaluated with feedback from 30 experienced fashion practitioners, confirming its feasibility and effectiveness. Insights from case studies conducted with two Hong Kong-based fashion companies further highlight the benefits and challenges of integrating GenAI into fashion design. While GenAI demonstrates promise in enhancing communication, accelerating design processes, and improving customer engagement and satisfaction, key challenges remain, including the need for high-quality datasets, significant computational resources, and ethical considerations related to AI-generated designs. The design principles derived from this study provide a structured guideline for system designers, offering a practical framework for developing GenAI systems that cater to the specific needs of the fashion industry. By contributing both theoretical and practical insights, this study advances understanding of how GenAI can drive innovation in fashion design and lays a foundation for future research in this domain.

In this study, artificial intelligence (AI) orientation, AI capabilities, as well as process-oriented dynamic capabilities (PDCs) within the realm of AI business value creation, are unpacked through multiple case studies in Hong Kong. We propose a conceptual framework suggesting that AI resources enable organizations to develop PDCs, manifesting in several abilities, thereby contributing to business value. In addition, the case study's findings indicate that AI capabilities developed by organizations correlate with their AI orientation, which is their overall strategic direction and aspiration of employing AI technology. Apart from basic AI capabilities, AI-oriented organizations would develop advanced AI capabilities. The proposed conceptual framework and findings can guide and assist practitioners in utilizing AI resources and building AI capabilities. This study also enriches the growing body of research on AI and contributes to the limited understanding of AI capabilities in the extant literature.

: People following the latest fashion trends gives importance to the popularity of fashion items. To estimate this popularity, we propose a model that comprises feature extraction using Inception v3 (a kind of Convolutional Neural Network) and a popularity score estimation using Multi-Layer Perceptron regression. The model is trained using datasets from Amazon (5,166 items) and Instagram (98,735 items) and evaluated by using mean-squared error, which is one of the many metrics of the performance of our model. Results show that, even with a simpler structure and requiring less input, our model is comparable with other more complicated methods. Our approach allows designers and manufacturers to predict the popularity of design drafts for fashion items, without exposing the unannounced design at social media or comparing with a large quantity of other items.

This study seeks to thoroughly understand the organizational context in which Artificial Intelligence (AI) would be implemented, through a systematic review and analysis of articles published (up to 2021) in 31 journals on information systems, business, management, and operations management. Seventy themes are identified from the literature and categorized into organizational, information systems, technological, and people dimensions for the antecedents, challenges, guidelines, and consequences of AI implementation in organizations. A conceptual framework for understanding AI implementation in organizations is also proposed. This study provides a research agenda to guide future research and facilitate knowledge accumulation and creation on AI implementation.

This study seeks to thoroughly understand the organizational context in which Artificial Intelligence (AI) would be implemented, through a systematic review and analysis of articles published (up to 2021) in 31 journals on information systems, business, management, and operations management. Seventy themes are identified from the literature and categorized into organizational, information systems, technological, and people dimensions for the antecedents, challenges, guidelines, and consequences of AI implementation in organizations. A conceptual framework for understanding AI implementation in organizations is also proposed. This study provides a research agenda to guide future research and facilitate knowledge accumulation and creation on AI implementation.

As artificial intelligence (AI) has recently gained momentum and attention, the interest and investment in AI have also accelerated. However, the impact of AI on firm value is rarely discussed. On the basis of the 119 announcements of 62 listed firms who have invested in AI, this study finds that AI investment has a negative impact on the firms' market value. The stock prices of the firms decrease by 1.77% on the day of the announcement. Nonmanufacturing firms and firms with weak information technology capabilities or low credit ratings suffer a more negative impact compared with other firms. The findings suggest that investors perceive AI investment announcements to be unwelcome news for the majority of firms. Subsequently, the characteristics affecting the shareholders' reaction towards AI adoption are presented. This research offers one of the first empirical evidence about the market value of AI and provides a reference for firms interested in investing in AI.

This study proposes an intelligent knowledge-based conversational agent system architecture to support customer services in e-commerce sales and marketing. A pilot implementation of a chatbot for customer services is reported in a leading women's intimate apparel manufacturing firm. The proposed system incorporates various emerging technologies, including web crawling, natural language processing, knowledge bases, and artificial intelligence. In this study, a prototype system is built in a real-world setting. The results of the system prototype evaluation are satisfactory and support the contention that the system is effective. The study also discusses the challenges and lessons learned during system implementation and the theoretical and managerial implications of this study.

In recent decades, many firms have invested in artificial intelligence (AI) and used them in business applications across industries. However, understanding of the impact of AI on firm value is limited. In this study, 67 AI investment announcements of 42 listed firms were analyzed by adopting the event study methodology. Results reveal that AI investment has a negative impact on a firm's market value, as the stock price of the firms decreased by 1.77% on the day of the announcement. The result also shows that comparing with stable market environment, investors response more negatively to AI investment announcements under unstable market environment.

In this study, we proposed a combined approach, which amalgamates machine learning and lexicon-based approaches for multiple-domain sentiment classification that supports Cantonese-based social media analysis. Our study contributes to the existing literature not only by investigating the effectiveness of the proposed combined approach for supporting social media analysis in the Cantonese context but also by verifying that the proposed method outperforms the baseline approaches, which are commonly used in the literature. We demonstrated that social media network-based classifiers can be general classifiers that support multiple-domain sentiment classification.

This paper aims to use soft systems methodology (SSM) to identify management support system opportunities for managing energy and utility usage in textile manufacturing processes. It presents an approach based on SSM to analyze the complex situation of developing an effective energy and utility management support system (EUMSS). This involves the identification of the scope of the system and user requirements, conceptual modeling of complex problem situations, identification of actors and decision processes, and information-needs modeling. The current study pioneers the examination of the application of SSM to the development of a novel EUMSS and contributes to the body of information systems knowledge in the context of EUMSS design. There appears to be limited academic research in the field of energy and utility system development and, in particular, in the area of EUMSS design, and none in the area of the application of SSM to EUMSS design. In addition, the modeling process could be beneficial if EUMSS design ideas could be widely shared and discussed. The identified scope and system requirements can serve as a guideline for designing and developing an effective EUMSS for textile processing.

This paper describes the design and development of a context-aware fleet management system (CFMS) prototype for real-time accident handling in logistics using a design science approach. One of the most important decisions in fleet management is the optimization of vehicle scheduling during an accident, such as a vehicle breakdown and mechanical failure during delivery. The schedule planner has to assign another vehicle to take over the task; thus, accident handling needs the reassignment or re-scheduling of vehicles. The large number of available vehicles for reassignment and numerous trips in a day make rescheduling complicated and difficult to resolve. In this paper, we propose a CFMS integrated with global positioning system (GPS) for real-time vehicle positioning and eSeal enabled by the RFID technology, to help human planners with rescheduling. A CFMS prototype was built and evaluated in a real-world setting. The system prototype was satisfactory during evaluation. The system was found to be more effective by its potential users and field logistics experts in aiding real-time accident handling in logistics. The design science approach used to develop the prototype could form a basis for further research.

A growing number of organisations around the world are considering the implementation of radio frequency identification (RFID) systems to improve their business and operations processes. In this study, a multi-stage implementation framework is developed and evaluated through a case study. The framework provides practitioners with a better understanding of the various stages of the RFID implementation process, including guidelines and issues with RFID systems implementation that need to be considered. We illustrate the viability of this implementation framework through a case study analysis of a textile dyeing and printing mill in China. We hope that the proposed framework will provide practitioners with a holistic perspective of implementing RFID systems.

This paper describes a case study of the research and development of an intelligent context-aware decision support system (ICADSS) prototype for real-time monitoring of container terminal operations in Hong Kong. We present the system design and development of the prototype system, and discuss the experiences and lessons learned. To the best of our knowledge, this study is the first identifiable application of an intelligent context-aware decision support system for the real-time monitoring of container terminal operations reported in the academic literature. The intelligent context-aware decision support system employs ZigBee-based ubiquitous sensor network (USN) technology. In this study, an ICADSS prototype was built and implemented in a real world setting. The results of the system prototype evaluation are satisfactory and support the contention that it is more effective in supporting the real-time tracking and tracing of container trucks, quay cranes, and rubber-tired gantry cranes in a container terminal. The results also validate the practical viability of the proposed system architecture. Given the contextual details of the study, we present the lessons learned from developing and operating the system in a container terminal and provide suggestions for further research. We hope that the proposed system architecture and developed prototype system can help both practitioners and academics in the further use and research of intelligent context-aware decision support systems.

System functionalities of matched filtering, channel banding, and wavelength demultiplexing are demonstrated using a unique MEMS-actuated microdisk resonator filter with dynamically tunable bandwidth. Error free operation of demultiplexed channel is realized.

A monolithic 4/spl times/4 wavelength-selective cross-connect (chip area = 3.2/spl times/4.6 cm ) is realized by integrating four 4/spl times/1 MEMS wavelength-selective switches and four 1/spl times/4 passive splitters, together with a 4/spl times/4 waveguide shuffle network on a silicon-on-insulator. Wavelength-selective routing has been successfully demonstrated.

A novel vertically coupled tunable microdisk resonator with integrated MEMS tunable optical couplers is demonstrated. A reconfigurable add-drop filter based on this device shows an extinction ratio of 20 dB

A monolithic 1/spl times/4 wavelength-selective switch is realized by integrating silicon planar lightwave circuits and MEMS micromirrors on silicon-on-insulator (1.4/spl times/2 cm/sup 2/) for CWDM networks with 20-nm spacing.