Journal of Systems and Information Technology

Publisher:: Emerald Group Publishing Limited
Publication date:: 2021-02-01

ISBN:: 1328-7265

Issue Number

No. 27-1, January - December - December - November - November - November - November 2024

Latest documents

Understanding customer opinions on IoT applications implemented in the retail industry worldwide and its implications for businesses in Vietnam
Purpose: This study aims to deeply understand customer experiences toward Internet of Things (IoT) applications in retail by developing machine learning models for aspect-based sentiment analysis (SA). It includes creating a related terms dictionary and proposing implications for retail businesses in Vietnam based on these analyses. The ultimate goal is to gain insights into customer opinions and assist administrators in formulating effective digital transformation and business strategies within the Vietnamese market. Design/methodology/approach: Initially, this research uses qualitative methods to identify different aspects of customer experience at stores equipped with IoT applications. Then, quantitative methods were applied through classification machine learning models which were trained on the annotated data set to classify comments into aspects and sentiments. Finally, the classification results were analyzed and visualized to draw implications about customer opinions of these stores. Findings: This study collected 77,042 customers’ comment from potential and actual customers who have ever shopped at retail stores with IoT applications deployed worldwide, identified ten new aspects of customer experience in this field and built a dictionary of related terms. Furthermore, this study contributed two efficient ensemble models with an accuracy of 81% and 89% for analyzing aspects and customer sentiments, respectively. This study also proposes implications for managers regarding the use of IoT technology in retail stores to improve shopping experiences for customers. Originality/value: This study’s findings help managers develop appropriate digital transformation and business strategies for integrating IoT technology into retail stores, especially for retail businesses in the Vietnamese market based on the analysis results and proposed model.
Examining the impact of artificial intelligence capability on dynamic capabilities, organizational creativity and organization performance in public organizations
Purpose: This study aims to evaluate an artificial intelligence (AI) capability scale using resource-based theory and tests its impact on dynamic capabilities and organizational creativity to influence the performance of public organizations. Design/methodology/approach: The study used qualitative and quantitative methods to develop and validate an AI capability scale using an integrative psychometric approach. An initial set of 26 items was selected from the literature for qualitative analysis. Self-reported data from 344 public managers in United Arab Emirates public organizations were used for scale refinement and validation. Hypotheses were tested against theoretically related constructs for nomological validation. Findings: A 23-item AI capability scale was developed. Nomological testing confirmed that AI capability positively and significantly enhances dynamic capabilities, which in turn boosts organizational creativity and improves organizational performance. Originality/value: Previous information system literature has not sufficiently addressed the importance of organizational-level complementary resources in developing distinctive capabilities within public organizations. Grounded in resource-based theory and recent AI research, this study provides a framework for public sector organizations to assess their AI capabilities. The findings empirically support the proposed theoretical framework, showing that AI capability increases dynamic capabilities, organizational creativity and performance.
Scourge of replacing contemporary work environment with artificial intelligence (AI-dark-side): the role of capacity development in quality of work-life and organisational performance
Purpose: The emergence of artificial intelligence (AI) which operates through technology and digital workspace has proven to transform organisations in recent times. However, there has been key concern over its efficiency among the workforce on how it may replace human intelligence in the contemporary work environment. This study aims to investigate the drawbacks otherwise known as the dark side of AI and its effect on employee quality of work−life and organisational performance through the lens of employee capacity development in reducing its shortcomings. Design/methodology/approach: This study used a descriptive research design using a cross-sectional survey approach to administer the research instrument to 1,847 customer service officers of banks, customer agents of telecoms, customer care of retail organisations in Nigeria business environment across various units were respondents of this study, however, 862 participants were finally used. A simple random strategy was used to survey the study participants, and existing scales were adopted to form a new research instrument. A partial least square (PLS) based structural equation model (SEM) was adapted to analyse the collected data from the respondents. Findings: The outcome of the study indicated that AI lacks creativity and has a negative impact on both employee quality of work−life and overall organisational performance. The outcome of the study demonstrated the drawbacks and the dark sides of AI as lack of emotional intelligence, lack of in-depth contextual knowledge, over-reliance on data quality and lack of ethical and moral decision analysis are the possible dark side of AI which adversely affect quality of work−life and overall performance of the organisations. The study concluded that it is difficult to replace human intelligence because of AI’s drawbacks and dark side. AI cannot function effectively beyond what is programmed in the system. Originality/value: This study has offered a novel trajectory against the efficiency and possible benefits of AI that people are familiar with. It has changed the understanding of the researchers, policymakers and organisations that AI cannot replace human intelligence in the workplace without improvement on those established AI dark sides.
Implementation of a chatbot in a unified communication channel
Purpose: This study aims to propose an architecture and presents the implementation of a unified chatbot that faces the challenges of heterogeneous communication channels. This approach enables the interaction with the chatbot to be carried out over multiple communication media on a single platform. Design/methodology/approach: The chatbot was embedded in a unified communications framework. Furthermore, it has been developed and tested using the information and communications technology (ICT)Core platform. Three test scenarios have been considered in the context of a digital marketing company, which include the use of multiple channels such as text, audio and e-mail. Usability and empirical tests were performed to collect both qualitative and quantitative data. Findings: The results indicate that the proposed model improves the completion rate and enables the chatbot to interact with the customer by capturing information over multiple channels. The findings also reveal that digital marketing organizations can use a unified chatbot in their marketing campaigns, which contributes to improving the quality of customer interaction, message personalization and continuous learning throughout the process. Originality/value: While the use of a chatbot is a relatively common practice among companies, its integration into unified communications networks is an emerging topic. Proposals for integration into a unified communication channel have mainly focused on access to the same account and conversations from multiple devices or access platforms. This approach, while useful, does not allow for the integration of information from multiple sources. Alternatively, an integrated architecture is suggested in which a chatbot obtains knowledge from multiple sources and uses it to increase the quality of communication with the customer.
A framework to guide and support the design of a smart city based on the PDCA cycle approach
Purpose: Information and communication technologies brought a new paradigm that allows policymakers to ground their actions on real-time events. Smart cities were initially conceived as a technological vision separate from urban planning. As a result, projects were rarely connected between departments, objectives were not aligned with strategic goals and there was a lack of citizen participation. This study aims to propose a framework to guide and support the design and implementation of a smart city. Design/methodology/approach: Interviews with eight policymakers and one secretary of state are conducted to explore current decision-making processes, specifically, to understand if and how smart city strategies are designed and who their main contributors are. Based on these findings, an inductive thematic analysis of existing literature studies to inspire the steps of the proposed framework is performed. Finally, these steps are discussed in a focus group with nine smart city experts to characterize the guidelines comprehensively. Findings: Policymakers confirmed the lack of a standard method and approach to orient their smart city strategies. Results describe a flexible, participatory framework that envisions 12 steps divided into 4 phases with dedicated guidelines. Originality/value: This paper integrates the plan-do-check-act cycle approach into the thinking for urban planning design. In addition, it raises the need to reflect on the definition of a country’s strategic plan and the alignment and execution of cities’ roadmaps.
Are we neglecting the influence of national culture (individualism–collectivism index) in mitigating the instances of data breach?
Purpose: Based on neo-institutional theory, this study evaluates factors that affect instances of data breaches in a hospital. The authors study the effect of adopting the health information exchange (HIE) initiative on a hospital’s data breach threats. This study integrates formal and information institutional factors to identify the antecedents that influence data breaches when adopting HIE. This study uses a hospital’s entrepreneurial orientation (EO) as a formal institutional factor and national culture (collectivism–individualism) as an informal institutional factor. Design/methodology/approach: Using a Statistical Analysis System, the authors analyze US hospital observations over five years. The data was collected from the Health Information and Management Systems Society (HIMSS) database, the Health and Human Services website and the Vandello and Cohen (1999) collectivism index. Findings: This study finds that when hospitals adopt HIEs, data breaches increase. This study also finds that both EO (formal institutional factor) and the individualism–collectivism index (informal institutional factor) significantly moderate these instances. Research limitations/implications: HIMSS has not updated its data set to reflect recent hospital data, so this study’s data set lacks recent data on US hospitals. Originality/value: This study is one of the few studies to address the impact of cultural variation in US hospitals and how it interacts with entrepreneurial activity to lower data breach threats when adopting new data exchange standards.
Understanding use continuance of social networking sites in organizations from employees’ perspectives: multicontextual contrasts between Canada and Cote d’Ivoire
Purpose: Limited research has espoused a comparative perspective to study social networking sites’ (SNS) use continuance despite most of them being abandoned after initial adoption. Most existing empirical works have been undertaken in western contexts, and they do not consider country-origin influence. Thus, they are of little benefit to global and transnational organizations. Awareness of countries’ similarities and contrasts provides the basis for understanding people’s behaviors in cross-cultural contexts, which can be crucial to ensuring technology acceptance and success, especially in multinational organizations. Our research aims to explain why and how people use SNSs sustainably in the workplace through a model and comparative study. Design/methodology/approach: The theoretical framework was developed to integrate and extend two major behavioral adoption and technology use models in explaining SNS use continuance. This paper collected data through a survey and analyzed it using structural equation modeling through partial least squares (PLS). Findings: One major contribution of this study is to highlight that the users in selected countries are driven strongly by subconscious factors rather than traditional factors based on the system attributes and users’ perceived rationality of continuing to use SNSs. Research limitations/implications: This paper recommends that the model in this study be tested in other technology environments to evaluate the external validity of the research study. The research was based on an unspecified platform, but each SNS may have its own singularities that should merit further consideration. Originality/value: This paper will contribute to the literature by integrating and extending two major theoretical frameworks and espousing a cross-national perspective.
Self-presentational concerns and lurking among users on social networking sites: an empirical study based on a moderated mediation model
Purpose: The phenomenon of nonposting behavior, known as lurking, has become increasingly prevalent on social networking sites (SNS). This study aims to understand why certain users are inclined to lurk on SNS by proposing a theoretical framework that integrates self-presentational concerns, SNS fatigue and social presence. Design/methodology/approach: Building upon the theoretical framework, a moderated mediation model is established to illustrate the mechanisms of lurking on SNS. Survey data were collected from 616 SNS users through an online survey and analyzed using the SPSS macro PROCESS. Findings: The findings show that self-presentational concerns have positive and direct effects on lurking. Moreover, the relationship between self-presentational concerns and lurking is partially mediated by SNS fatigue. Furthermore, both the direct effect and the mediating effect are moderated by social presence. Originality/value: This study offers a novel theoretical perspective on lurking behavior by introducing a moderated mediation model. The findings reveal intricate mechanisms underlying this specific SNS usage behavior and its connections to both self-presentational concerns and SNS fatigue, thereby enriching the existing literature on user engagement and inactivity on SNS. Furthermore, this research highlights the pivotal role of social presence in moderating the effects of self-presentational concerns, offering new insights into the dynamics of online social interactions.
An experimental design of the blockchain business model using a soft system dynamics modeling approach
Purpose: The growing discussion on blockchain and business models often falls short of demonstrating and evaluating systems consistently exposed to settings of dynamic complexity. Therefore, in practicing systems thinking, this study aims to provide a depiction of dynamic complexity in blockchain business models and develop policy-based scenarios to enhance blockchain-based systems behavior. Design/methodology/approach: This study integrated the soft system dynamic (SD) methodology approach, which focuses on a situation analysis and SDs in policy design. This single case study chose a firm engaged in the content industry, where the adoption of blockchains is a solution to tackle the industry’s significant challenges. Data were collected using a qualitative approach and then adapted into a simulation model. Findings: The study pinpointed key parameters significantly affecting the system through a sensitivity analysis. Then, this experimental study found that all improvement initiatives delivered better system performance. At the same time, the study also identified counterintuitive findings, where the interventions using multiple value subsystems had insignificant effects on the system compared to a single advent. Originality/value: This study illuminates the growing field of blockchain and business models through system modeling and experimentation, using an integrative approach like soft system dynamics methodology. It also identifies and demonstrates value distribution and the dynamic complexity inherent in the blockchain business model.
Com_Tracker: a two-phases framework for detecting and tracking community evolution in dynamic social networks
Purpose: This paper aims to introduce a novel modularity-based framework, Com_Tracker, designed to detect and track community structures in dynamic social networks without recomputing them from scratch at each snapshot. Despite extensive research in this area, existing approaches either require repetitive computations or fail to capture key community behavioral events, both of which limit the ability to generate timely and actionable insights. Efficiently tracking community structures is crucial for real-time decision-making in rapidly evolving networks, while capturing behavioral events is necessary for understanding deeper community dynamics. This study addresses these limitations by proposing a more efficient and adaptive solution. It aims to answer the following questions: How can we efficiently track community structures without recomputation? How can we detect significant community events over time? Design/methodology/approach: Com_Tracker models dynamic social networks as a sequence of snapshots. First, it detects the community structure of the initial snapshot using a static community detection algorithm. Then, for each subsequent time step, Com_Tracker updates the community structure based on the previous snapshot, allowing it to track communities and detect their changes over time. The locus-based adjacency encoding scheme is adopted, and Pearson’s correlation guides the construction of neighboring solutions. Findings: Experiments conducted on various networks demonstrate that Com_Tracker effectively detects community structures and tracks their evolution in dynamic social networks. The results highlight its potential for real-time tracking and provide promising performance outcomes. Practical implications: Com_Tracker offers valuable insights into community evolution, helping practitioners across fields such as resource management, public security, marketing and public health. By understanding how communities evolve, decision-makers can better allocate resources, enhance targeted strategies and predict future community behaviors, improving overall responsiveness to changes in network dynamics. Originality/value: Com_Tracker addresses critical gaps in existing research by combining the strengths of modularity maximization with efficient tracking of community changes. Unlike previous methods that either recompute structures or fail to capture behavioral events, Com_Tracker provides an incremental, adaptive framework capable of detecting both community evolution and behavioral changes, enhancing real-world applicability in dynamic environments.