Assessing cloud computing value in firms through socio-technical determinants

Motivational Understanding of MOOC Learning: The Impacts of Technology Fit and Subjective Norms

This study examines the mechanisms underlying the relationship between motivation and massive open online course (MOOC) learning intention, and the contextual moderators that affect this mechanism. Drawing on motivational theory and the related literature, this study investigates how motivation affects students' intention to learn with MOOCs and the related meditation and moderation effects. The findings of our study show that both extrinsic and intrinsic motivations have a significant influence on intention to learn with MOOCs. In addition, the results also suggest that the relationship between extrinsic motivation and intention to learn is mediated by the technology fit perceived by learners but not the relationship between intrinsic motivation and intention to learn. Furthermore, the relationships between both extrinsic and intrinsic motivation and intention to learn are moderated by subjective norms. This study enriches the extant literature on the impact of technology fit and subjective norms on MOOC learning. Few studies have focused on how students can be effectively encouraged to take MOOCs. Given the lack of theoretical and empirical research, this study developed a theoretical model and conducted an empirical study to fill the research gap.

Analytical study on users' awareness and acceptability towards adoption of multimodal biometrics (MMB) mechanism in online transactions: a two-stage SEM-ANN approach

The study analyses user awareness of multimodal biometrics and its acceptability for online transactions in the current dynamic world. The study was performed on the five underlying perspectives: User Acceptability, Cognizant Factors towards Biometrics, Technological factors, Perceptional Factors (Fingerprints, Iris, Face Recognition and Voice) and Data Privacy Factors. A questionnaire was prepared and circulated to the 530 biometrics users; on that basis, the corresponding answer was obtained for analysis. SEM is first employed to gauge the research model and test the prominent hypothesized predictors, which are then used as inputs in the neural network to evaluate the relative significance of each predictor variable. By considering the standardized significance of the feed-for-back-propagation of ANN algorithms, the study found a significant effect of DPF_3 (93%), DPF_2 (50%) and DPF_4 (34%) on the adoption of MMB. In the Perceptional construct, PRF_2 (49%) and PRF_3 (33%) was relatively the most important predictor, whereas, in User Acceptability, UAC_2 (37%), UAC_3 & UAC_5 (41%) was vital to be considered. Only one item, TCF_2 (35%), from Technological Factors, followed by Cognizant factors, i.e., CFG_1 (33%), confirmed the best fit model to adopt MMB. The research is a novel effort when compared to past studies as it considered cognizant and perceptual factors in the proposed model, thereby expanding the analytical outlook of MMB literature. Thus, the study also explored several new and valuable practical implications for adopting multimodal instruments of biometrics along with certain limitations.

Key Challenges and Opportunities for Cloud Technology in Health Care: Semistructured Interview Study

Background

The use of cloud computing (involving storage and processing of data on the internet) in health care has increasingly been highlighted as having great potential in facilitating data-driven innovations. Although some provider organizations are reaping the benefits of using cloud providers to store and process their data, others are lagging behind.

Objective

We aim to explore the existing challenges and barriers to the use of cloud computing in health care settings and investigate how perceived risks can be addressed.

Methods

We conducted a qualitative case study of cloud computing in health care settings, interviewing a range of individuals with perspectives on supply, implementation, adoption, and integration of cloud technology. Data were collected through a series of in-depth semistructured interviews exploring current applications, implementation approaches, challenges encountered, and visions for the future. The interviews were transcribed and thematically analyzed using NVivo 12 (QSR International). We coded the data based on a sociotechnical coding framework developed in related work.

Results

We interviewed 23 individuals between September 2020 and November 2020, including professionals working across major cloud providers, health care provider organizations, innovators, small and medium-sized software vendors, and academic institutions. The participants were united by a common vision of a cloud-enabled ecosystem of applications and by drivers surrounding data-driven innovation. The identified barriers to progress included the cost of data migration and skill gaps to implement cloud technologies within provider organizations, the cultural shift required to move to externally hosted services, a lack of user pull as many benefits were not visible to those providing frontline care, and a lack of interoperability standards and central regulations.

Conclusions

Implementations need to be viewed as a digitally enabled transformation of services, driven by skill development, organizational change management, and user engagement, to facilitate the implementation and exploitation of cloud-based infrastructures and to maximize returns on investment.

Examining the Critical Success Factors in IT Projects

Many companies experience IT project failures in relatively new areas such as Big Data, Data Science, Enterprise Systems, Blockchain, Cloud Computing, IT security, and IoT. Because of inadequate research in identifying critical success factors for these projects, we conducted a Delphi study employing separate panels for each of two kinds of project implementations, those using a predictive lifecycle approach and those using an adaptive lifecycle approach. We found common critical success factors: user/client involvement, senior management support, effective monitoring and control, effective communication and feedback, good change and configuration management, having a competent project manager, proper project leadership, and excellent vendor performance. Both predictive and adaptive lifecycle IT projects had certain unique critical success factors. The findings provide guidance to IT Project Managers.

Industry 4.0 transition: a systematic literature review combining the absorptive capacity theory and the data–information–knowledge hierarchy

Purpose

The effect of the transition toward digital technologies on today’s businesses (i.e. Industry 4.0 transition) is becoming increasingly relevant, and the number of studies that have examined this phenomenon has grown rapidly. However, systematizing the existing findings is still a challenge, from both a theoretical and a managerial point of view. In such a setting, the knowledge management (KM) discipline can provide guidance to address such a gap. Indeed, the implementation of fundamental digital technologies is reshaping how firms manage knowledge. Thus, this study aims to critically review the existing literature on Industry 4.0 from a KM perspective.

Design/methodology/approach

First, the authors defined a structuring framework to highlight the role of Industry 4.0 transition along with absorptive capacity (ACAP) processes (acquisition, assimilation, transformation and exploitation), while specifying what is being managed, that is data, information and/or (actual) knowledge, according to the data-information-knowledge (DIK) hierarchy. The authors then followed the systematic literature review methodology, which involves the use of explicit criteria to select publications to review and outline the stages a process has to follow to provide a transparent and replicable review and to analyze the existing literature according to the theoretical framework. This procedure yielded a final list of 150 papers.

Findings

By providing a clear picture of what scholars have studied so far on Industry 4.0 transition, in terms of KM, this literature review highlights that among all the studied digital technologies, the big data analytics technology is the one that has been explored the most in each phase of the ACAP process. A constructive body of research has also emerged in recent years around the role played by the internet of things, especially to explain the acquisition of data. On the other hand, some digital technologies, such as cyber security and smart manufacturing, have largely remained unaddressed. An explanation of the role of these technologies has been provided, from a KM perspective, together with the business implications.

Originality/value

This study is one of the first attempts to revise the literature on Industry 4.0 transition from a KM perspective, and it proposes a novel framework to read existing studies and on which to base new ones. Furthermore, the synthesis makes two main contributions. First, it provides a clear picture of the different digital technologies that support the four ACAP phases in relation to the DIK hierarchy. Accordingly, these results can emphasize what the literature has looked at so far, as well as which digital technologies have gained the most attention and their impacts in terms of KM. Second, the synthesis provides prescriptive considerations on the development of future research avenues, according to the proposed research framework.

Does cloud computing influence enterprise performance?

The emergence of the cloud computing service has resulted in the entry of many companies into the market, with numerous competitors for cloud computing leading the technological trend. Recent studies have mainly focused on the characteristics of cloud computing, whereas its influence on firm performance has been rarely discussed. Therefore, this study aimed to fill this empirical research gap. This current study examined whether investments in cloud computing can influence firm performance and whether cloud computing influences firm performance. Data were collected using a questionnaire. Structural equation modeling provided evidence supporting the theoretical model. The results of this study revealed that cloud computing investment influences firm learning and growth performance, internal processes, and finances. The theoretical and practical contributions and implications of these findings are described in this paper.

Factors Affecting Cloud Computing Adoption and Continuance Intention of Students in Thailand

An increasing number of higher education institutions have embraced Cloud Computing Services (CCS) to better respond to the issues arising from the COVID-19 pandemic. Cloud computing has helped to ease the process and lower the cost of offering online education and hybrid learning. However, some universities in Thailand face cloud computing adoption challenges because students lack awareness of the benefits and risks of CCS. Therefore, it is vital to identify the critical factors affecting the initial and continuance adoption of CCS by students in less developed countries. This study adopts a trade-off lens to assess the impact of the perceived usefulness and perceived risks regarding students’ attitudes toward the initial and continuing adoption of CCS. Using a survey of CCS from a large public university in Thailand, we found that performance expectation and effort expectation positively affect perceived usefulness, and that authentication risks positively affect perceived risks. We also found that perceived usefulness rather than perceived risks is a deciding factor in adopting CCS. Higher education institutions in Thailand can accelerate the adoption of CCS by improving students’ perceived performance and reducing the perceived risks.