The Diffusion of Predictive Analytics for Innovative Interactive Marketing Strategy in International E-Fashion Commerce

Small and medium enterprises (SMEs) around the world engage in e-commerce and e-business to support business operations as well as to enhance revenue generation from nontraditional sources. Internet and e-business technologies (IEBT) are the pillars of e-commerce and e-business. Despite the universal appeal of IEBT, it has been reported that the adoption of such technologies by SMEs is influenced by contextual imperatives. The objective of this research is to investigate factors impacting the adoption of IEBT in SMEs based in the Maritime region of Canada. A research model based on the diffusion of innovation (DIT) and the Technology–Organization–Environment (TOE) frameworks was used to guide the discourse. Such factors as relative advantage, compatibility, complexity, management support, organizational readiness, external pressure, and government support were used to develop relevant hypotheses. Questionnaires were mailed to key informants in SMEs. Data analysis was performed using the partial least squares (PLS) technique. Predictions related to relative advantage, management support, and competition's pressure were confirmed. The study did not support the constructs of compatibility, complexity, government support, customers' and partners' pressures as significant predictors of IEBT adoption by the SMEs in the region.

Digital transformation is increasingly recognized as a key driver of sustainable business development and effective change management in today’s economy. This study examines the factors influencing digital transformation decisions among trade and service firms in Can Tho City, a major economic hub in Vietnam’s Mekong Delta. By combining the Technology-Organization-Environment (TOE) framework and the Diffusion of Innovations (DOI) theory with principles of sustainable business, the study adopts a quantitative approach using survey data from 187 enterprises. The results identify seven key factors affecting digital transformation: relative advantage, organizational readiness, competitive pressure, government support, compatibility, managerial support, and pressure from partners and consumers. Among these, relative advantage is the most influential driver, while partner and consumer pressure have a negative impact, reflecting the challenges small and medium-sized enterprises (SMEs) face in adapting to change with limited resources. This study contributes to both theory and practice by offering a comprehensive framework for understanding digital transformation in the context of sustainable development. It provides valuable insights for policymakers and business leaders in emerging economies, emphasizing the importance of aligning technological change with sustainable business practices through effective change management strategies.

Business organizations around the world engage in e-commerce (EC) and e-business to support business operations and enhance revenue generation from non-traditional sources. Studies focusing on EC adoption in Sub Saharan Africa (SSA) are just beginning to emerge in the extant information systems (IS) literature. The objective of this current study is to investigate factors impacting the acceptance of EC in small businesses in SSA with Nigeria as an example. A research model based on the Diffusion of Innovation (DIT) and the Technology–Organization–Environment (TOE) frameworks were used to guide this discourse. Such factors as relative advantage, compatibility, complexity, management support, organizational readiness, external pressure, and IS vendor support were used to develop relevant hypotheses. Questionnaires were administered to respondents in Nigeria and data analysis was performed using the Partial Least Squares (PLS) technique. Predictions related to relative advantage, management support, and IS vendor support were confirmed; the other hypotheses were unsupported by the data. The study’s implications for research and practice are discussed in the chapter.

The recent rise of artificial intelligence (AI) has sparked interest among scholars in various fields, particularly public relations (PR) and strategic communication (SC). As the digital landscape evolves and competition intensifies, integrating AI technology into PR and SC practices becomes increasingly essential. This study aims to identify factors influencing AI adoption in PR and SC agencies using the technology–organization–environment (TOE) framework. Structural equation modeling was employed to analyze the relationships between influencing factors—client pressure, competitive pressure, compatibility, complexity, relative advantage, top management support, and organizational readiness—and the intention to adopt AI. A survey of 277 PR and SC agencies across the Arab world was conducted for data collection. The analysis indicated that client pressure, competitive pressure, relative advantage, and organizational readiness positively impact AI adoption, while complexity negatively affects it. Additionally, relative advantage mediates the relationship between competitive pressure and AI adoption, and organizational readiness mediates between compatibility and AI adoption, as well as between top management support and AI adoption. The study discusses the theoretical contributions and practical implications of these findings.

In the digital era, small and medium enterprises (SMEs) adopt AI for business operations to enhance firm performance. The adoption of AI is driven by multiple factors influencing the business landscape, while ethical principles may also impact adoption. The aim of this study is to develop a model that explains factors influencing AI adoption by SMEs to enhance business performance, by integrating the technology-organization-environment (TOE) framework with the diffusion of innovation (DOI) theory and ethical principles. The study employed a quantitative method, using a self-administered questionnaire disseminated among 150 respondents from South African SMEs and analysed using structural equation modelling (SEM). The results showed that compatibility, top management support, organisational readiness, employee capability, customer pressure, vendor support, fairness, accountability, and transparency significantly influence AI adoption, while relative advantage, complexity, high costs, and competitive pressure were less significant. The study concludes that AI adoption is key to enhancing the performance of South African SMEs.

Grounded in the Technology-Organisation-Environment (TOE) framework, this study identifies the main factors affecting the intention to adopt Big Data Analytics (BDA) in supply chain management (SCM) for firms based in Saudi Arabia. This study focuses on identifying and analysing the role of competitive pressure as a contextual variable that can moderate the effects of these factors on the adoption intention. A survey of 220 IT managers revealed that compatibility, relative advantage, and top management support are positively perceived factors as they foster the firms’ intentions to adopt BDA in SCM. Their effects on intentions were positively moderated by competitive pressure as a contextual variable. However, BDA complexity and organisational readiness were not supported as influencing firms’ intentions to adopt BDA. The statistical analyses also indicated that the effects of complexity and organisational readiness on intentions are not significantly moderated by competitive pressure. This study contributes to the literature by emphasising the interaction between TOE factors, instead of considering them separately. It also offers guidance to managers aiming to adopt and use BDA in SCM.

This study examines the determinants of Artificial Intelligence (AI) adoption and its influence on employee engagement in Egyptian organizations, utilizing the Technology–Organization–Environment (TOE) framework. Based on survey data from 210 professionals across various industries and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings reveal that relative advantage, compatibility, and top management support significantly drive AI adoption. In contrast, complexity, security/privacy concerns, organizational readiness, competitive pressure, and external and government support do not show significant effects. Furthermore, AI adoption demonstrates a positive but modest impact on employee engagement, suggesting the presence of other contributing factors. These results reflect the unique socio-economic conditions in Egypt, where internal organizational factors outweigh external influences in shaping technology adoption. The findings have practical implications for organizations and policymakers, emphasizing the need to prioritize internal drivers—such as leadership commitment, perceived benefits, and system compatibility—when promoting AI adoption. Additionally, organizations should align AI initiatives with employee needs and workplace culture to enhance engagement outcomes.

Supply chain risk management (SCRM) has become a strategic priority as global networks face increasing turbulence from pandemics, geopolitical conflicts, economic volatility, and climate-related disruptions. Artificial intelligence (AI) is widely recognized as a transformative enabler for predictive and prescriptive risk analytics, yet its practical adoption is often constrained by technical and organizational barriers. Low-code and no-code AI platforms have recently emerged as democratizing tools that lower entry barriers, enabling non-programmers to design, deploy, and scale intelligent workflows with greater accessibility. Despite this promise, scholarly research explicitly focusing on low-code AI in the context of SCRM remains scarce. This study addresses this gap by integrating bibliometric and text-mining approaches with a technology management perspective. A dataset of 62 publications retrieved from the Web of Science Core Collection was analyzed through bibliometric mapping to identify influential works, collaboration structures, and thematic clusters. Complementing this, Latent Dirichlet Allocation (LDA) topic modeling of 45 abstracts uncovered four distinct thematic groups. While the dominant clusters revolve around AI-driven resilience, digital transformation, and cybersecurity, a marginal but emerging theme reflects low-code and no-code adoption, highlighting its nascent role in SCRM research. Building on these findings, the paper proposes a conceptual model that synthesizes Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Diffusion of Innovation (DOI), and the Technology–Organization–Environment (TOE) framework. The model introduces accessibility-driven resilience as a capability linking low-code AI adoption to organizational outcomes. The study contributes by (i) mapping the intellectual landscape of AI-enabled SCRM, (ii) theorizing low-code AI adoption as a managerial decision in technology management, and (iii) outlining implications for practitioners, particularly SMEs, seeking resilience through accessible AI solutions. The findings further indicate that low-code and no-code adoption, though marginal in the current literature, is emerging as a distinct research stream, underscoring the concept of accessibility-driven resilience.

Cloud computing is a new information technology paradigm, one which has been adopted in many different sectors. Many developing countries are working on improving e-government to provide services to their citizens that are integrated and effective. Therefore, the Saudi government has made improving e-government a high priority. The aim of this study is to explore significant factors affecting the adoption of cloud computing in e-government services in Saudi Arabia as a case study. After identifying these factors, we propose a new comprehensive model for the Adoption of Cloud Computing in Saudi G-GOVernment (ACCE-GOV) that has been adopted from the Technology Organisation Environment (TOE) framework and the Diffusion of Innovations (DOI) theory. This research model examines factors of technological context (compatibility, complexity, service quality, security, and relative advantages), organisational context (top management support, organisation size, and technology readiness), environmental context (regulations and competitive pressures), and social context (awareness, trust, and attitude) in regard to the adoption of cloud computing. Ultimately, this study enhances the decision-making of the Saudi government through understanding and highlighting the most important factors that influence the adoption of cloud computing in an e-government context. This is a position paper, reporting on the work in progress. The findings of this study will be useful for the researchers who considers suitability of different methodological approaches for cloud computing adoption and evaluation.

Artificial Intelligence (AI) is increasingly recognized as a key driver of economic growth, innovation, and enhanced efficiency across public services. In a Muslim-majority country like Malaysia, where the religion is recognized as the state's official religion, the widespread adoption of AI should take into consideration ethical and religious compatibility alongside technological and organizational readiness. Despite its transformative potential, AI adoption in public services remains constrained by concerns related to Shariah compliance, trust, and governance. This study introduces a conceptual model, TOEM (Technology–Organization–Environment–Maqasid), to guide AI adoption in Malaysia's public service landscape. The model is grounded in the Technology–Organization–Environment (TOE) framework and Diffusion of Innovation (DOI) theory, and uniquely integrates Maqasid Shariah principles as a moderating factor to ensure alignment with Islamic ethical foundations. The development of the TOEM model is informed by a comprehensive review of existing technology adoption theories, Islamic ethical frameworks, and preliminary fieldwork involving public agencies in Selangor. Findings highlight key barriers such as limited infrastructure, lack of expertise, and ethical concerns surrounding AI deployment. By aligning AI strategies with both conventional determinants and Islamic principles, TOEM provides a structured pathway for responsible and inclusive AI integration. Without such a model, Malaysia risks hindering AI adoption, thereby missing out on its economic and societal benefits. This model offers policymakers and practitioners a culturally coherent and values-driven framework to harness AI's full potential, mitigating adoption risks while maintaining public trust and adherence to national and religious values.

This article explores the role of innovation strategies for enhancing the competitiveness of SMEs in global markets. Based on a literature review of innovation strategies, these strategies were separated into four groups: product innovation, process innovation, organizational innovation, and marketing innovation. The study focused only on the product innovation strategy and marketing innovation strategy. The empirical analysis found that Algerian enterprises adopt both a product innovation strategy and a marketing innovation strategy. There are differences regarding the relationship between the product innovation strategy and adding value to the firm compared to the relationship between the marketing innovation strategy and adding value to the firm in Algerian enterprises. Here, there are three positive relationships between product innovation strategy and adding value to the firm whereas in contrast, there are two positive relationships between marketing innovation strategy and adding value to the firm. Thus, the enterprises under study must shift from a product innovation strategy to a marketing innovation strategy, which means focusing their attention more on the latter to market their products more and faster, which allows them to add more value to the company.

In this research, we look at the antecedents of accounting information system (AIS) usage and their impact on the effectiveness of AIS. Therefore, drawing upon the resource-based view (RBV) and the technology–organization–environment (TOE) framework, we propose an integrated model to investigate the precursors and impacts of AIS utilization among Jordanian small and medium-sized enterprises (SMEs). The model so proposed incorporates the usage of AIS and its performance aspects into one framework. The current research utilized a self-administered questionnaire survey to collect data. Using data drawn from 186 respondents, the research model was verified empirically in the context of Jordanian SMEs. The findings revealed that compatibility, competitive pressure, organizational readiness, the commitment of the owners/managers and government supports have exerted a significant influence on the usage of AIS. In addition, the results demonstrated that AIS usage significantly influenced the effectiveness of AIS. In addition, firm size moderated the effect of AIS usage on AIS effectiveness. The findings of the current study afford insights as to how firms might garner improved usage of an AIS to gain better firm performance and provide contributions to the small but developing stream of research examining both antecedents and impacts of IS/IT usage.

ABSTRACTBig data analytics has revolutionised business competition by uncovering hidden patterns in raw data, enabling better decision‐making, increased productivity, knowledge generation and enhanced innovation. With the rapid expansion of big data, it is essential to seek and implement solutions to manage these datasets and extract valuable insights and knowledge from them. Although prior research has attempted to describe the role of traditional database management systems, there is still a scarcity of empirical evidence on the role of big data analytics adoption by small businesses in an emerging market such as India. The authors contribute to the existing body of knowledge by analysing the factors influencing big data analytics adoption in SMEs and predicting the potential impact on their market and financial performance. The ‘partial least square‐structural equation modelling (PLS‐SEM)’ technique was utilised to analyse data. This research provides a strong research model for big data analytics applications in which one of the two proposed technological factors (‘perceived usefulness’), one organisational factor (‘top management support’) and two environmental factors (‘competitive pressure and government rules and regulations’) significantly influence big data analytics adoption. Additionally, the results of this article may serve as a catalyst, encouraging SMEs to embrace big data analytics more willingly and recognising its potential benefits in enhancing business processes, decision‐making, and overall competitive strength in the market. Grounded in the ‘Technological‐Organisational‐Environmental (TOE) framework, resource base view (RBV) and technology acceptance model (TAM)’ theories, this article aimed to measure the causal association between the drivers and outcomes.

The rapid emergence of generative AI (GAI) presents significant opportunities and complexities for startups, particularly in culturally distinct ecosystems such as Thailand. This work explores how Thai startups evaluate the appropriateness and responsible adoption of GAI, drawing on the Technology-Organization-Environment (TOE) framework, Diffusion of Innovation (DOI) theory, and the Resource-Based View (RBV). Based on semi-structured interviews with 11 toplevel experts from sectors including FinTech, DeepTech, and AgriTech, we developed the eight-dimensional T-GAINS framework. These dimensions are mapped onto a metatheoretical space that distinguishes internal vs. external/systemic origins and descriptive vs. normative orientations. Our findings revealed that GAI adoption is not only shaped by technical fit and organizational readiness but also affected by knowledge integration and organizational ethical commitment. The results offer practical guidance for startups navigating innovation amid uncertainties and evolving ethical expectations.

This article investigates the adoption of Industrial Internet-of-Things (IIoT) in Auto-Component Manufacturing SMEs (ACM SMEs) in the context of a developing country like India by using the Technology-Organization-Environment (TOE) framework. This research surveyed Information & Communication Technology (ICT) officers, managers and owners of 320 ACM SMEs in India using a structured questionnaire to understand the adoption of IIoT. The primary data was analyzed using the PLS-SEM technique. It was found that IIoT expertise, IIoT infrastructure, relative advantage, compatibility, cost, security, organizational readiness, top management support, competitive pressure, and support from technology vendors are factors that affect the adoption of IIoT. This article considered organization size as the control variable. The results show that it does not have a significant effect on the adoption of IIoT. ACM SMEs are one of the important sectors adopting IIoT. This article provides valuable insights to their managers and IIoT vendors. It also suggests key inputs to the government officials involved in the ‘Make in India' initiative of Government of India (GoI) and Ministry of Micro, Small and Medium Enterprises (MSME).