Understanding SaaS adoption: The moderating impact of the environment context

The influence mechanism analysis on the farmers' intention to adopt Internet of Things based on UTAUT-TOE model

Internet of Things (IoT) technologies are widely recognized as one of the most important infrastructures for economic development and technological innovation. By analyzing the influencing factors of vegetable farmers' intention to adopt agricultural IoT, it helps to formulate effective IoT promotion policies and accelerate the realization of agricultural modernization. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology-Organization-Environment (TOE) theory, this study constructed for the first time a mechanism model of the influence of vegetable farmers' intention to adopt IoT, expanding the scope of current research on agricultural IoT and revealing the intrinsic influence mechanism of farmers' adoption of IoT technologies. In this study, 357 quantitative data were obtained by a questionnaire survey, and structural equation modeling was used to test the direct and indirect effects of each factor on vegetable farmers' intention to adopt IoT. The results show that almost all variables in TOE have significant direct impacts on the intention, while no variables in UTAUT have significant direct impacts. Among variables in TOE, government support and complexity are the two most important elements influencing the intention. Although the interactions among variables in TOE and UTAUT are also found, the indirect effects of variables are non-significant. Therefore, it is proposed to reduce the complexity of operation and use of IoT technologies; improve rural information infrastructure and compatibility of IoT platforms and devices; and governments should increase subsidies, and incentives to promote the use of IoT in agriculture and agricultural practices.

Determinants of cloud computing integration and its impact on sustainable performance in SMEs: An empirical investigation using the SEM-ANN approach

Although extant literature has thoroughly investigated the incorporation of cloud computing services, examining their influence on sustainable performance, particularly at the organizational level, is insufficient. Consequently, the present research aims to assess the factors that impact the integration of cloud computing within small and medium-sized enterprises (SMEs) and its subsequent effects on environmental, financial, and social performance. The data were collected from 415 SMEs and were analyzed using a hybrid SEM-ANN approach. PLS-SEM results indicate that relative advantage, complexity, compatibility, top management support, cost reduction, and government support significantly affect cloud computing integration. This study also empirically demonstrated that SMEs could improve their financial, environmental, and social performance by integrating cloud computing services. ANN results show that complexity, with a normalized importance (NI) of 89.14%, is ranked the first among other factors affecting cloud computing integration in SMEs. This is followed by cost reduction (NI = 82.67%), government support (NI = 73.37%), compatibility (NI = 70.02%), top management support (NI = 52.43%), and relative advantage (NI = 48.72%). Theoretically, this study goes beyond examining the determinants affecting cloud computing integration by examining their impact on SMEs' environmental, financial, and social performance in a comprehensive manner. The study also provides several practical implications for policymakers, SME managers, and cloud computing service providers.

Factors for the implementation of the circular economy in Big Data environments in service companies in post pandemic times of COVID-19: The case of Colombia

In emerging economies, Big Data (BD) analytics has become increasingly popular, particularly regarding the opportunities and expected benefits. Such analyzes have identified that the production and consumption of goods and services, while unavoidable, have proven to be unsustainable and inefficient. For this reason, the concept of the circular economy (CE) has emerged strongly as a sustainable approach that contributes to the eco-efficient use of resources. However, to develop a circular economy in DB environments, it is necessary to understand what factors influence the intention to accept its implementation. The main objective of this research was to assess the influence of attitudes, subjective norms, and perceived behavioral norms on the intention to adopt CE in BD-mediated environments. The methodology is quantitative, cross-sectional with a descriptive correlational approach, based on the theory of planned behavior and a Partial Least Squares Structural Equation Model (PLS-SEM). A total of 413 Colombian service SMEs participated in the study. The results show that managers' attitudes, subjective norms, and perceived norms of behavior positively influence the intentions of organizations to implement CB best practices. Furthermore, most organizations have positive intentions toward CE and that these intentions positively influence the adoption of DB; however, the lack of government support and cultural barriers are perceived as the main limitation for its adoption. The research leads to the conclusion that BD helps business and government develop strategies to move toward CE, and that there is a clear positive will and intent toward a more restorative and sustainable corporate strategy.

Onsite/offsite social commerce adoption for SMEs using fuzzy linguistic decision making in complex framework

There has been a growing social commerce adoption trend among SMEs for few years. However, it is often a challenging strategic task for SMEs to choose the right type of social commerce. SMEs usually have a limited budget, technical skills and resources and want to maximise productivity with those limited resources. There is much literature that discusses the social commerce adoption strategy for SMEs. However, there is no work to enable SMEs to choose social commerce-onsite/offsite or hybrid strategy. Moreover, very few studies allow the decision-makers to handle uncertain, complex nonlinear relationships of social commerce adoption factors. The paper proposes a fuzzy linguistic multi-criteria group decision-making in a complex framework for onsite, offsite social commerce adoption to address the problem. The proposed approach uses a novel hybrid approach by combining FAHP, FOWA and selection criteria of the technological-organisation-environment (TOE) framework. Unlike previous methods, the proposed approach uses the decision maker's attitudinal characteristics and recommends intelligently using the OWA operator. The approach further demonstrates the decision behaviour of the decision-makers with Fuzzy Minimum (FMin), Fuzzy Maximum (FMax), Laplace criteria, Hurwicz criteria, FWA, FOWA and FPOWA. The framework enables the SMEs to choose the right type of social commerce considering TOE factors that help them build a stronger relationship with current and potential customers. The approach's applicability is demonstrated using a case study of three SMEs seeking to adopt a social commerce type. The analysis results indicate the proposed approach's effectiveness in handling uncertain, complex nonlinear decisions in social commerce adoption.

Cloud accounting adoption in Thai SMEs amid the COVID-19 pandemic: an explanatory case study

The COVID-19 pandemic has altered the way small and medium-sized businesses (SMEs) function. To maintain business efficiency and reduce operating costs in the context of the constraints imposed by the pandemic, SMEs have been forced to embrace new digital technologies. Cloud accounting is becoming an increasingly important business operating tool for SMEs. By adopting cloud-based accounting, SMEs can become more efficient, financially organized, and flexible. This study aims to investigate the factors that have a pervasive influence on cloud accounting adoption among SMEs in Thailand, following the pandemic's effects. This study integrates three technology adoption theories-the Technology-Organization-Environment framework, Diffusion of Innovation theory, and Institutional Theory-and studies them alongside SMEs' unique characteristics. The research employs a qualitative case study method and triangulated sources of evidence. The findings provide important implications for the research community, policymakers, cloud accounting vendors, and SME owners aiming to formulate better approaches to cloud accounting adoption after the pandemic. The results suggest that vendors should focus on SMEs' particular characteristics and needs. By contrast, SMEs should determine the organizational fit of the cloud accounting platform and integrate cloud accounting with other aspects of their operations.

The Critical Factors Impacting Artificial Intelligence Applications Adoption in Vietnam: A Structural Equation Modeling Analysis

The concept of artificial intelligence (AI) is the transformation of inanimate objects into intelligent beings that can reason similarly to humans. Computer systems are capable of imitating a number of human intelligence functions, such as learning, reasoning, problem solving, speech recognition, and planning. In this regard, artificial intelligence applications have been developed to assist corporations and entrepreneurs in making business decisions. Hence, the aim of the study is to investigate the adoption of AI applications at the Vietnamese organizational level. Using the core structures of the technology–organization–environment (TOE), the theoretical model was constructed based on how technical and environmental elements influence companies’ technological innovation adoption decisions. Ten critical factors related to AI adoption are identified. To test the model, data were obtained from 193 senior managers who are directly in charge of information systems in both private and public companies in Vietnam. Subsequently, the Structural Equation Modeling (SEM) approach was used to analyze the data. The findings indicate that technical compatibility, relative advantage, technical complexity, technical capability, managerial capability, organizational readiness, government involvement, market uncertainty, and vendor partnership are significantly related to AI application adoption. Interestingly, the study results indicated that the relationship between organization size and AI adoption was not statistically significant. Therefore, the suggested adoption of the AI application could contribute to the existing research on the adoption of AI through the TOE. Finally, the significant government law implications and future research directions are further addressed.

Adoption of Green Industrial Internet of Things to Improve Organizational Performance: The Role of Institutional Isomorphism and Green Innovation Practices

Emerging technology developments and functions of the Internet of Things (IoT) in industrial systems are leading the development of the Industrial IoT (IIoT). Greener, i.e., cleaner environmental goals can be achieved by putting green IIoT (GIIoT) into practice. This research aims to explore the reasons for the adoption of GIIoT in organizational decision-making and to explore its impact on organizational performance. The proposed research model was tested by collecting data through a structured questionnaire. The findings suggest that institutional isomorphism has a positive impact on the adoption of GIIoT. Moreover, GIIoT is positively associated with green innovation (GI) practices (e.g., product, process, and management) that lead to organizational performance. The potential impact of various types of institutional isomorphism described in this study can help organizations better comprehend the institutional pressures they enforce and/or appease their stakeholders, especially as they adopt GIIoT, to manage production issues and potential compliance pressures in the process.

Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs

Big data (BD) analytics has been increasingly gaining attraction in both practice and theory in light of its opportunities, barriers and expected benefits. In particular, emerging economics view big data analytics as having great importance despite the fact that it has been in a constant struggle with the barriers that prevent its adoption. Thus, this study primarily attempted to determine the drivers of big data analytics in the context of a developing economy, Jordan. The study examined the influence of technological, organizational and environmental factors on big data adoption in the Jordanian SMEs context, using PLS-SEM for the analysis. The empirical results revealed that the relative advantage, complexity, security, top management support, organizational readiness and government support influence the adoption of BD, whilst pressure of competition and compatibility appeared to be of insignificant influence. The findings are expected to contribute to enterprise management and strategic use of data analytics in the present dynamic market environment, for both researcher and practitioner circles concerned with the adoption of big data in developing countries.

Implementing Vertical Integration in the Industry 4.0 Journey: Which Factors Influence the Process of Information Systems Adoption?

One of the key principles of Industry 4.0 is the implementation of vertical integration, which considers the integration of information systems from different hierarchical levels in a company to support decision-making with real-time data flows. Companies face challenges when they want to implement vertical integration, which is not trivial due to the risks inherent to the decision stages of adoption. We investigate the main factors influencing the different stages of adoption of vertical integration to provide a clearer view of what managers should consider at each stage. We adopt a multi-case study approach based on the investigation of ten companies that followed this adoption process. We develop a framework with 22 factors deployed in the three stages of decision (knowledge, persuasion, and final decision) and three main dimensions of analysis: technology, organization, and environment. We analyze the potential tensions between these factors and show how managers should balance such factors during the decision stages.

Organizational Challenges of Development and Implementation of Virtual Reality Solution for Industrial Operation

This research investigated the organizational challenges related to the development and implementation of virtual reality (VR) technology for operation in a conservative heavy machinery industry. The incorporation of a VR solution for heavy machinery equipment enhanced the safety and convenience of operation under dangerous work conditions. However, the development and implementation processes faced challenges. Furthermore, the adoption of the solution by users was perceived to be slower than anticipated. We aimed to explore the main challenges that the developer organization faced and how it also influenced user organizations. Due to the exploratory nature of the research, qualitative analysis was chosen, interviews were conducted, and thematic analysis was applied. The themes and subthemes were identified and discussed. The results showed the existence of challenges related to technology maturity, managerial challenges regarding communication and support coordination, workload, and multiple stakeholder management. The findings emphasize the importance of attending to the existing and potential organizational challenges before and throughout technological innovation. Theoretical and managerial implications are discussed, and a future research agenda is suggested.

Using extended complexity theory to test SMEs' adoption of Blockchain-based loan system

Blockchain-based loan system can be summed up as: information exchange between various government departments; information exchange between enterprises and various financial institutions; detection of the actual use of loans in the form of encrypted currency. This technology is supposed to reduce a lot of financing costs for SMEs on average. Therefore, this research extends complexity theory to discover the factors that affect the use of Blockchain loan systems by SMEs. Complexity, perceived risk, perceived fairness and reward sensitivity prove to have significant effects on usage intention. Complexity proves to have moderating effects on other relationships. This research may contribute to the system performance improvement and provide opportunities for SMEs to share information with financial institutions or individuals around the world, thereby providing investors with equal opportunities for competition.

Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations

Purpose

Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.

Design/methodology/approach

This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.

Findings

This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.

Practical implications

This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.

Originality/value

This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.

Adoption of Automatic Warehousing Systems in Logistics Firms: A Technology–Organization–Environment Framework

The adoption of automatic warehousing systems, a type of green technology, has been an emerging trend in the logistics industry. In this study, we develop a conceptual model using a technology–organization–environment framework to investigate the factors which influence logistics firms to adopt green technology. Our model proposes that the adoption of green technology is influenced by perceived advantage, cost, technological turbulence, business partner influence, firm size, firm scope and operational performance. The objective of this study is to identify the conditions, as well as the contributing factors, for the adoption of automatic warehousing systems in logistics firms. Data were collected from 98 firms in China, and structural equation modeling with partial least squares is adopted to analyze the data. The results suggest that high perceived relative advantage, firm size, cost, firm scope, operation performance, technological turbulence and influence of business partners are important factors affecting IT adoption in small businesses. Therefore, decision support should be provided for enterprises from the three aspects of technology, organization and environment to improve the adoption of automatic warehousing systems.

Switching intention to crypto-currency market: Factors predisposing some individuals to risky investment

We investigate factors affecting individual investors' switching intention from traditional financial market to crypto-currency financial market. By sampling factors of individual investors related with crypto-currency (CC), the study applies structural equation modeling method (SEM) to investigate their effects on switching intention by integrating PPM and Reinforcement Sensitivity theories (RST) to form a pulling, pushing and mooring effects model. The investigation indicates that crypto-currency market can be regarded as a kind of beneficial supplement of tradition investment market for those individual investors who are with high innovativeness, reward sensitivity, knowledge and perceived risk. This study proves that the individual investors are not only attracted by significant expected return from crypto-currency but also relevant knowledge and risks disclosed by crypto-currency market regulators and distributors. The findings reinforce major roles for both market regulators and individual investors in considering and providing insights for future policy, management and investigations.

Understanding the determinants of GIT post-adoption: perspectives from Mozambican institutions

This study aims to understand the determinants of geographical information technologies at the scale of post-adoption use and intention to increase the level of use in Mozambican institutions. Three known theories (diffusion of innovation theory, technology - organisation - environment framework, and institutional theory) have been used in order to accomplish the study. The data analysis showed that the variables compatibility, geographical scope, expansion opportunities, and normative pressure contribute to explaining GIT use. Relative advantage, complexity, coercive pressure, and mimetic pressure contribute to explaining the intention to increase GIT levels of use. The model revealed substantial power of explanation for GIT post-adoption.

Understanding the Determinants for the Adoption of Mobile Market Research: An Empirical Study in the Spanish Market Research Industry

The aim of this study is to investigate the determinants for the intention to adopt mobile technology as a data collection methodology in market research projects. A conceptual framework was developed using the technology-organization-environment (TOE) model to identify technological factors (perceived benefits and limitations), organizational factors (open attitude toward change, professional competence, satisfaction with traditional systems, and firm size), and environmental factors (industry pressure, client pressure, and participant pressure) affecting adoption. The empirical study was performed with data from 67 firms in the Spanish market research industry, which were analyzed using partial least squares (PLS). The results suggest that only organizational and environmental factors have a significant influence on adoption. Key factors include professional competence, organizational openness, satisfaction with traditional and online methodologies, and pressure from industry, clients and survey participants. The findings reveal that technological characteristics are no longer a driver, as firms are starting to adopt mobile marketing research based on its greater convenience for participants, and as an element of strategic differentiation.