Search Results (68)

The rapid growth of e-commerce has intensified last-mile delivery challenges in Thailand, where rising parcel volumes contribute to failed deliveries and increased carbon emissions. Smart parcel lockers offered a promising solution, though their adoption depends on consumer acceptance. However, existing research has not sufficiently examined how trialability, performance expectancy, and perceived risk operate alongside core TAM beliefs within an integrated framework, particularly in emerging markets. This study extends the Technology Acceptance Model (TAM) with constructs from Diffusion of Innovation (DOI) theory and the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine the determinants of smart parcel locker adoption, incorporating trialability, performance expectancy, and perceived risk. A quantitative survey was conducted with 397 Thai consumers with prior online shopping and parcel delivery experience. Data were analyzed using covariance-based structural equation modeling (CB-SEM). The model explained 79.3%, 94.3%, and 82.7% of the variance in perceived ease of use, attitude, and intention. Trialability is the strongest predictor, working through perceived ease of use, while attitude and performance expectancy together drove intention. Contrary to traditional TAM, perceived usefulness did not significantly affect attitude, and perceived risk had no significant effect. These findings suggest that, for simple self-service delivery technologies, first-hand experience is more influential than emphasizing usefulness or safety concerns. This contributes to technology acceptance theory and offers practical guidance to increase smart parcel locker usage. Full article

Purpose: This study provides a comprehensive bibliometric analysis of AI integration in banking, mapping research trends, intellectual structures, and performance-related themes. While AI’s growing importance in financial services has attracted scholarly attention, bibliometric studies specifically focusing on the AI-banking-performance triad remain limited. This study addresses this gap by systematically mapping the research dynamics, intellectual structure, and thematic evolution of this domain. Materials and Methods: Publications were retrieved from the Scopus database using predefined search criteria, resulting in 891 articles published between 2014 and 2024. Bibliometric indicators were employed to examine publication trends, authorship patterns, and institutional contributions. VOSviewer (version 1.6.20) and the R-based Bibliometrix (version 4.3.3) package were used to construct co-authorship networks, keyword co-occurrence maps, co-citation structures, and thematic maps. Results: Findings reveal exponential growth, particularly after 2018, with a peak of 277 articles in 2024. IEEE Access and Expert Systems with Applications are the leading sources, while Baesens B. emerges as a highly influential author. China and India dominate output, though European countries achieve higher per-article impact. Highly cited works focus on credit scoring, fraud detection, fintech, and financial inclusion. Conceptual mapping identifies five thematic clusters, with “AI in banking” as a motor theme, NLP as a niche, and credit detection as emerging. Conclusions: AI research in banking is rapidly expanding, interdisciplinary, and globally distributed. Theoretically, the findings are framed by TAM, TPB, DOI, and Dynamic Capabilities Theory, revealing that AI adoption represents a multi-level phenomenon spanning individual acceptance, institutional diffusion, and strategic reconfiguration. These theoretical lenses explain why fraud detection and credit scoring dominate as early adoptions while NLP and governance remain underdeveloped. The study highlights key contributors, emerging themes, and future research directions. However, findings are constrained by reliance on a single database (Scopus), exclusion of non-English and non-peer-reviewed sources, and inherent limitations of bibliometric methods. Full article

Enterprise systems are widely adopted in organizations, yet user acceptance remains a major challenge due to the complex interplay of cognitive, social, motivational, and innovation-related factors. Existing technology acceptance models often provide fragmented explanations by focusing on limited determinants. This study proposes TAM 4, an exploratory framework integrating constructs from the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Hedonic-Motivation System Adoption Model (HMSAM), and Diffusion of Innovation (DOI). The study was conducted in the context of enterprise application usage and professional enterprise system training environments involving organizational users, trainees, and practitioners. Data were collected from 115 enterprise system users (trainees and practitioners). To consolidate overlapping indicators and strengthen construct definition, principal component analysis (PCA) was applied, yielding seven higher-order constructs that explain 81.642% of cumulative variance. The framework was validated using PLS-SEM with three scenario-based structural models (full mediation, partial mediation, and direct effects). The results show that Model 3 provides the best fit and predictive performance (SRMR = 0.048; NFI = 0.786), indicating that enterprise system adoption is better explained through a direct effect structure rather than a purely mediated TAM pathway. The novelty of this study lies in introducing TAM 4 as a PCA-driven multi-theory acceptance model and evaluating its explanatory robustness through multi-scenario model comparison, offering practical insights for improving enterprise system implementation strategies. Full article

The adoption of disruptive technologies has become increasingly critical for organizations, particularly following the global shifts prompted by the COVID-19 pandemic. Despite the potential benefits, many organizations, including those in the Fast-Moving Consumer Goods (FMCG) industry, face significant hurdles in this transition. Consequently, this study aims to understand the primary challenges and enabling factors influencing the adoption of disruptive technologies for sustainable digital transformation within the South African FMCG sector. A quantitative methodology was employed, utilizing a questionnaire for data collection. Data from 102 respondents were analyzed using SPSS version 28, involving descriptive statistics (mean item score) to rank factors and exploratory factor analysis (EFA) to identify underlying constructs, and a reliability test was carried out with a score of 0.7. Key challenges identified include high initial costs and poor collaboration. Prominent enabling factors include top management commitment and operational cost reduction. The EFA revealed significant underlying challenge dimensions such as “Infrastructural and Resources Constraints” and “Human Factors Constraints,” and enabling dimensions including “Organizational Commitment and Strategy” and “Leadership.” The study concludes with key implications for promoting successful adoption. The adoption of disruptive technologies has become a strategic imperative for sustainable digital transformation (SDT), particularly in emerging markets such as South Africa’s FMCG sector. This study investigates the key challenges and enabling factors shaping technology adoption within this context. A quantitative methodology was employed, using a structured questionnaire distributed to 102 professionals across FMCG organizations in Gauteng. Exploratory factor analysis (EFA) revealed latent dimensions within both challenges and enablers, which were then interpreted through the lens of Rogers’ Diffusion of Innovation (DOI) theory. To enhance analytical clarity, a matrix model was developed linking factor dimensions to DOI attributes such as relative advantage, complexity, compatibility, trialability, and observability. The study found that high initial costs, poor collaboration, and human capability gaps significantly impede adoption, while strong leadership, strategic alignment, and operational cost savings facilitate it. The findings underscore the need for systemic interventions that address not only technical readiness but also leadership, organizational culture, and structural alignment. Practical implications are outlined for both policy and management, particularly in leveraging DOI attributes to accelerate digital transformation, as well optimize innovation diffusion within resource-constrained environments. For the future, the study proposed a hybrid methodology incorporating qualitative interviews to enhance depth and suggests longitudinal tracking to capture temporal shifts in transformation maturity. Full article

A program library, libang77, for computing pure spin-angular coefficients for any one- and scalar two-particle operator is presented. The method is based on the combination of the second quantization and quasi-spin techniques with angular momentum theory and the method of irreducible tensorial sets. A non-relativistic approach is used, in which relativistic corrections may be included in the Breit–Pauli approximation. This program library, libang77, is integrated into the Atomic Structure Package ATSP2K [ATSP2K, C. Froese Fischer, G. Tachiev, G. Gaigalas, and M.R. Godefroid, Comput. Phys. Commun. (2007). DOI: 10.1016/j.cpc.2007.01.006], but it can be implemented in other program packages too. Full article

COVID-19 vaccine hesitancy is well documented, but less is known about booster hesitancy among fully vaccinated adults. A qualitative approach was employed to identify factors affecting COVID-19 booster hesitancy using diffusion of innovation (DoI) theory. The study was conducted in Philadelphia, Pennsylvania. In-depth interviews (n = 30) were done with adults, including those who had (n = 9) and had not (n = 21) been boosted. Participants were categorized into DoI adopter groups or a “refuser” group for those with no intention of getting boosted. Transcripts were analyzed using an iterative coding process with consensus and triangulation to develop thematic categories. Participants had a mean age of 41 and were 63.3% Black; 20% were classified as innovators, 6.7% early adopters, 3.3% early majority, 6.7% late majority, 43.3% laggards and 20% refusers. Three themes varied across groups: level of perceived risk susceptibility of getting COVID-19 in the future, information needs and levels of vaccine literacy, and effects of ongoing institutional mistrust. Those in the laggard and refuser groups generally had lower vaccine literacy, higher levels of institutional mistrust, and were more likely to listen to friends and family for booster advice, all consistent with DoI adopter characteristics. These differences indicate important intervention targets to promote booster uptake, especially in those who have been previously vaccinated. Full article

Intelligent Clinical Decision Support Systems (ICDSS) are increasingly integrated into healthcare settings to enhance clinical decision-making, efficiency, and patient safety. Despite advances in artificial intelligence-enabled decision support, ICDSS adoption remains inconsistent, particularly in complex clinical environments where professional autonomy, workflow alignment, and accountability are critical. This study examines healthcare providers’ perspectives on ICDSS through a grounded theory approach informed by established Information Systems theories, including the Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Acceptance Model (TAM), Diffusion of Innovation (DOI), and the Human-Organization-Technology fit (HOT-fit) framework. Semi-structured interviews were conducted with 11 providers within a large, integrated healthcare organization, and data were analyzed using open, axial, and selective coding. The findings reveal three interrelated dimensions shaping ICDSS use: provider experience, clinical utility, and adaptation. While ICDSS were perceived as valuable for improving efficiency, supporting treatment decisions, and enhancing patient safety, their adoption was constrained by cognitive overload, workflow misalignment, data quality concerns, and perceived threats to professional autonomy. Trust, explainability, and workflow fit emerged as central mechanisms influencing selective use rather than full adoption. By grounding provider perspectives within a sociotechnical lens, this study extends existing IS theories to the context of AI-enabled clinical decision support and offers empirically grounded insights for designing ICDSS that better align with clinical practice. Full article

The strength of a GPS carrier-to-noise ratio (CNR) signal is closely influenced by the multipath effect. This effect becomes more pronounced during flood events, as the reflection coefficient of water is significantly higher than that of dry soil. Consequently, the CNR measurements of GPS signals are impacted by floods. Based on this theory, the fluctuation of the CNR during a flood can be used to accurately monitor the process of a flood from occurrence to recession. Considering that the strength of the CNR largely depends on the satellite and frequency, and the characteristics of the influence of a flood on the CNR are different for each frequency, a new method based on the GPS dual-frequency direct signal CNR and the strength constraint threshold strategy was developed to increase the accuracy of the flood dynamics monitoring process. By using 64 MGEX (Multi-GNSS Experiment) stations distributed globally, an accurate direct-signal CNR threshold model of GPS dual-frequency was established. The threshold model demonstrated that the average difference of the direct-signal CNR, which is larger than 45 dB-Hz, between adjacent days at GPS L1 and L2 frequencies is 0.0659 dB-Hz and 0.0661 dB-Hz, respectively. Moreover, GPS real datasets in Zhengzhou city, China, from DOY (day of year) 199 to DOY 203, 2021, were collected to assess the proposed method. Based on the fluctuation of the direct-signal CNR threshold, the experimental results show that the flood appeared at about 16:04 PM on DOY 200, 2021, reached a peak at approximately 5:05 AM on DOY 202, and totally subsided at about 8:54 AM on DOY 202. Thus, the experiment results reveal that the proposed method accurately monitors the entire process of a flood from occurrence to recession, which provides valuable insights into operational flood dynamics, warning, and monitoring based on the GPS technique. Full article

India is one of the fastest growing economies with significant potential for the use of smart farming operations. Although agriculture is a major sector, implementation of smart technologies in the agriculture sector has not progressed in India. We use a mixed-methods approach to develop knowledge on the factors determining this slow adoption of smart technology and develop strategies for large-scale adoption in the Indian agriculture sector. First, qualitative interviews are used to understand the factors behind the slow diffusion of smart technology in the agriculture sector. Based on the responses, we link the results of the qualitative study from the agri-sector to the well-known Diffusion of Innovations (DoI) theory. We then develop a framework for applying Fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the impact of multiple causal factors. We apply our research findings to help achieve SDG 12 in the agriculture sector. Our findings indicate individual factors on their own may influence adoption, but some reasonable combinations of factors (e.g., a combination of technology, knowhow, experience, benefits-operation, and finance and reliability) could also result in the large-scale adoption of smart technologies in improving Indian agricultural operations. By doing so, we provide a contextual empirical configurational test of the DoI theory in the Indian smart agricultural context. Full article

Robo-advisors are evolving fintech solutions that ask potential clients about their investment purpose and time horizon and then offer investment strategies to reach different goals. This study aims to build on prior research and gain insights into the influence of innovation attributes (relative advantage, complexity, compatibility, and observability), perceived trust, and image regarding robo-advisor adoption by applying and extending the Diffusion of Innovation (DOI) theory. Data were collected using a cross-sectional survey approach. A total of 187 valid responses were obtained from an online participant recruitment website based in the United States and analysed using the partial least squares approach. The findings indicate that relative advantage and attitude influence an individual’s intention to adopt a robo-advisor, while all innovation attributes, perceived trust, and image of a robo-advisor influence an individual’s attitude towards it. By extending the DOI framework, this research advances understanding of its applicability to robo-advisor adoption. This study contributes to the literature by clarifying the influences on robo-advisor adoption and their relationships. From a practical standpoint, the findings and measures could help wealth management companies improve their promotional campaigns and technical design. Full article

As the population ages, autonomous shuttles (AS) present a potential solution for older adults’ mobility needs. However, acceptance—often assessed through hypothetical scenarios rather than lived experience—remains a significant barrier. This study aimed to explore older adults’ perceptions of AS through pre- and post-exposure, and to examine how these experiences shape their AS acceptance within the Diffusion of Innovations (DOI) framework. Using existing qualitative data from pre- and post-exposure focus groups, with 32 older adults across Florida, we used hybrid thematic analysis, grounded in DOI theory. The results revealed that the technology’s ease of use, as experienced when riding the AS (Trialability), reduced initial concerns related to Complexity. While participants acknowledged the Relative Advantage of AS in enhancing their mobility and safety, their acceptance was conditional upon addressing the AS’s slow speed and abrupt braking. Acceptance was also contingent upon Compatibility with personal lifestyles and the establishment of clear AS Regulations, to build trust. The findings indicate that for older adults, AS acceptance is a dynamic process where direct exposure is essential for overcoming initial concerns. However, widespread adoption will ultimately be influenced by AS performance, seamless integration of AS into their daily lives, and a robust regulatory framework. Full article

In the era of sustainable digital transformation, organizations increasingly rely on artificial intelligence (AI) to enhance efficiency, innovation, and long-term competitiveness. However, employees’ psychological barriers, including technostress and innovation resistance, continue to constrain successful and sustainable AI adoption. Grounded in Social Exchange Theory (SET), Conservation of Resources Theory (COR), Diffusion of Innovation Theory (DOI), and the Technology Acceptance Model (TAM), this study develops an integrated model linking perceived organizational support (POS)—comprising emotional, informational, and instrumental dimensions—to employees’ sustainable AI adoption through the dual mediating roles of technostress and innovation resistance. Based on 426 valid responses collected from multiple industries, a triadic hybrid approach combining Structural Equation Modeling (SEM), Artificial Neural Networks (ANNs), and Necessary Condition Analysis (NCA) was applied to capture both linear and nonlinear mechanisms. The results reveal that Informational Support (IFS) is the most influential factor and constitutes the sole necessary condition for high-level AI adoption, while emotional and instrumental support indirectly promote sustainable adoption by mitigating employees’ stress and resistance. This study contributes to sustainable management and AI adoption research by providing insights into the potential hierarchical and threshold patterns of organizational support systems in digital transformation. It also provides managerial implications for designing transparent, empathetic, and resource-efficient support ecosystems that foster employee-driven intelligent transformation. Full article

This study investigates the impact of Internet of Things (IoT) demand on Digital Economic Growth (DEG) in Saudi Arabia between 2015 and 2023, employing both linear regression and a panel Autoregressive Distributed Lag (ARDL) model. The results show a long-term, significant, and positive association between IoT adoption and DEG, supported by the Technology Organization Environment (TOE) framework, highlighting the relevance of technology readiness and organizational capacity. Moreover, Internet penetration is a significant driver of digital transformation, aligned with the Diffusion of Innovations (DOI) theory, which emphasizes the role of connectivity in facilitating the adoption of digital devices. IoT will have little or no impact in the short term, but in the long run, the benefits are clear. Furthermore, despite the long- and short-term benefits of 5G deployment indicated by the results, a divergence between 5G deployment and electricity consumption is signaled by the significance of the error-correction term, which may be attributed to infrastructure and deployment prerequisites. Additionally, as an extension of the Resource-Based View (RBV) paradigm, the ultimate drivers of DEG through innovation and strategic resources highlight the importance of Research and Development (R&D) investment and Foreign Direct Investment (FDI) in inducing its growth. In contrast, inflation has an adverse impact on DEG, confirming macroeconomic instability as an obstacle to digital advancement, which relates to the environmental pillar of TOE. Policymakers can maximize Saudi Arabia’s digital economic growth on a sustainable, stronger path by investing in IoT infrastructure, increasing internet access and adoption, enhancing R&D and institutional support, and addressing challenges related to macroeconomic stability and 5G deployment. This study adds to the extant research by empirically evaluating the short- and long-term effects of IoT adoption on Saudi Arabia’s digital economic development, thereby providing insights into the roles of innovation, infrastructure, and institutional support in driving digital transformation. Full article

Global environmental and sustainability concerns are increasingly pressuring industries in all developing economies to align their supply chain operations with ecological, social, and economic responsibilities. This study investigates the extent to which Sustainable Supply Chain Management (SSCM) enablers are influencing firm-level productivity in a developing economy, and how effectively the practices of SSCM mediate this relationship. This research aims to determine the extent to which Sustainable Supply Chain Management (SSCM) enablers influence firm-level productivity in a developing economy, and how effectively SSCM practices mediates this relationship. Building on the Diffusion of Innovation (DOI) theory, the research adopts a well-structured design and employs Structural Equation Modeling (SEM) to test the designed conceptual framework. The findings show that, while direct effects of enablers on productivity are limited, SSCM practices play a critical mediating role in translating these enablers into measurable performance-based improvements. The study contributes theoretical insights by extending DOI theory into the pharmaceutical supply chain context and offers practical guidance for managers and policymakers in developing economies by seeking to enhance competitiveness through sustainable practices. Full article

The adoption of artificial intelligence (AI) and smart technologies is reshaping global hospitality. However, in emerging markets, uptake remains limited by financial, organizational, and infrastructural barriers. This study examines the digital readiness of 1821 licensed accommodation providers in Albania, a rapidly expanding tourism economy, using an integrated framework that combines the Technology Acceptance Model (TAM), technology–organization–environment (TOE) framework, and Diffusion of Innovations (DOI). Data were collected via a structured survey and analyzed using descriptive statistics, exploratory factor analysis, cluster analysis, and structural equation modeling. Exploratory factor analysis identified a single robust readiness dimension, covering smart automation, environmental controls, and AI-driven systems. K-means segmentation revealed three adopter profiles: Tech Leaders (17.7%), Selective Adopters (43.5%), and Skeptics (38.8%), with statistically distinct but modest mean differences in readiness, reflecting stronger adoption in central urban and coastal hubs compared to weaker uptake in cultural heritage and non-urban regions. Structural modeling showed that environmental competitive pressure strongly enhanced perceived usefulness, which, in turn, drove behavioral intention, whereas perceived ease of use (operationalized as implementation complexity) had negligible effects. Innovation readiness was consistently associated with broader adoption, although intention was translated into actual use only among Tech Leaders. The findings highlight a fragmented digital ecosystem in which enthusiasm for AI exceeds its feasibility, underscoring the need for differentiated policy support, modular vendor solutions, and targeted capacity building to foster inclusive digital transformation. Full article