Manoj A. Thomas

Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors

Oliveira, T., Thomas, M., & Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & Management, 51(5), 497-510. DOI: 10.1016/j.im.2014.03.006

Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM

Mobile banking (mBanking) enables customers to carry out their banking tasks via mobile devices. We advance the extant body of knowledge about mBanking adoption by proposing a model for understanding the importance and relationship between the user perception of mBanking, initial trust in mBanking services, and the fit between the technology and mBanking task characteristics. We synergistically combine the strengths of three IS theories - task technology fit (TTF) model, unified theory of acceptance and usage of technology (UTAUT), and initial trust model (ITM). The model was tested in a study conducted in Portugal, one of the European Union (EU) countries with the highest mobile phone adoption. Based on the sample of 194 individuals we applied partial least squares (PLS) to test the conceptual model propose. The path significance levels were estimated using the bootstrapping method (500 resamples). The study found that facilitating conditions and behavioral intentions directly influence mBanking adoption. Initial trust, performance expectancy, technology characteristics, and task technology fit have total effect on behavioral intention. The paper offers valuable insights to decision-makers involved in the implementation and deployment of mBanking services. For researchers, the paper highlights the usefulness of integrating TTF, UTAUT and ITM in the development of a decision support framework to study the adoption of new technologies.

Full length articleMobile payment: Understanding the determinants of customer adoption and intention to recommend the technology

Mobile payment is receiving growing attention globally, from consumers to merchants, as an alternative to using cash, check, or credit cards. The potential of this technology is enormous. This study aims to identify the main determinants of mobile payment adoption and the intention to recommend this technology. We advance the body of knowledge on this subject by proposing an innovative research model that combines the strengths of two well-known theories; the extended unified theory of acceptance and use of technology (UTAUT2) with the innovation characteristics of the diffusion of innovations (DOI), with perceived security and intention to recommend the technology constructs. The research model was empirically tested using 301 responses from an online survey conducted in a European country, Portugal. Data was analyzed using the structured equation modeling (SEM). We found compatibility, perceived technology security, performance expectations, innovativeness, and social influence to have significant direct and indirect effects over the adoption of mobile payment and the intention to recommend this technology. The relevance of customers intention to recommend mobile payment technology in social networks and other means of communication was also confirmed, supporting the recommendation to include it in social marketing campaigns and in future technology adoption studies. For researchers this study provides a basis for further refinement of individual models of acceptance. For practitioners, understanding the key constructs is crucial to design, refine, and implement mobile payment services, applications, and products that achieve high consumer acceptance, value, and high rates of positive recommendations in social networks.

Going back to basics in design science: from the information technology artifact to the information systems artifact

The concept of the ‘information technology (IT) artifact’ plays a central role in the information systems (IS) research communitys discourse on design science. We pose the alternative concept of the ‘IS artifact’, unpacking what has been called the IT artifact into a separate ‘information artifact’, ‘technology artifact’ and ‘social artifact’. Technology artifacts (such as hardware and software), information artifacts (such as a message) and social artifacts (such as a charitable act) are different kinds of artifacts that together interact in order to form the IS artifact. We illustrate the knowledge value of the IS artifact concept with material from three cases. The result is to restore the idea that the study of design in IS needs to attend to the design of the entire IS artifact, not just the IT artifact. This result encourages an expansion in the use of design science research methodology to study broader kinds of artifacts.

A semantic approach to monitor business process

Finding an effective method for managing and evaluating the performance of business processes is a key element for e-business success.

Assessing the role of IT-enabled process virtualization on green IT adoption

Green Information Technology (IT) tools and practices contribute to environmental sustainability and business processes virtualization. To assess whether IT-enabled process virtualization capabilities impact organizational Green IT initiatives, Bose and Luo 2011 proposed a conceptual model that combines three theories: technology-organization-environment framework, process virtualization theory, and diffusion of innovation theory. We conducted an empirical analysis of data from 251 European firms, and found that environment context (competition intensity and regulatory support) was more important to Green IT initiatives than organization or technology context. Technology factors (sensory readiness, relationship readiness, synchronism readiness, and identification and control readiness) facilitating process virtualization were not found significant to organizational Green IT initiatives.

A snail shell process model for knowledge discovery via data analytics

The rapid growth of big data environment imposes new challenges that traditional knowledge discovery and data mining process (KDDM) models are not adequately suited to address. We propose a snail shell process model for knowledge discovery via data analytics (KDDA) to address these challenges. We evaluate the utility of the KDDA process model using real-world analytic case studies at a global multi-media company. By comparing against traditional KDDM models, we demonstrate the need and relevance of the snail shell model, particularly in addressing faster turnaround and frequent model updates that characterize knowledge discovery in the big data environment. Big data imposes many unique challenges for knowledge discovery via analytics.A snail shell process model for knowledge discovery via data analytics (KDDA) is proposed.Design science used to develop, refine and validate the snail shell model.The real world case demonstrates the relevance of snail shell model for organizational knowledge discovery.The study fills the gap in existing knowledge discovery and data mining (KDDM) literature.

An empirical analysis to assess the determinants of SaaS diffusion in firms

We propose a conceptual model for assessing the determinants of SaaS diffusion process (i.e. intention, adoption, and routinization). The conceptual model is based on three important theories of adoption at the firm level (i.e., technology-organization-environment TOE framework, diffusion of innovation DOI theory, and institutional INT theory). Data collected from 265 firms are used to test the conceptual model. Nine determinants are found to be statistically significant. By examining direct and total (direct and indirect) effects of the determinants, the study offers rich insight into the firms decision to adopt and routinize SaaS. An evaluation of factors influencing the diffusion of SaaS.Research model based on TOE framework, INT theory, and DOI theory.Direct effects, as well as total (direct and indirect) effects are evaluated.The study provides insights to decision makers considering SaaS strategies.

Interpreting Deep Structures of Information Systems Security

Confidentiality, integrity and availability, while being key requirements for ensuring security, have had limitations in providing an integral security model that incorporates the benefits of all predominant designs. Heeding to the call that security models cater to unique operational characteristics of individual firms, this paper presents a deep structure framework of information systems (ISs) security. The framework incorporates three models—representational model, which identifies subsystems within an organization; state-tracking model, which ensures that different states trace IS security in the real world;nd the decomposition model, which defines specific external events that are a stimuli to changes in internal events. In a final synthesis, the paper presents a deep structure-based conceptual clarity that manifests the meaning of dynamic, custom-fit and flexible IS security solutions.

Using Ontological Reasoning for an Adaptive E-Commerce Experience

As e-commerce applications proliferates the Web, the authors are often overwhelmed by the task of sifting through the copious volumes of information. Since the nature of foraging for information in such digital spaces can be characterized as the interaction between internal task representation and the external problem domain, the authors look at how expert systems can be used to reduce complexity of the task. They describe a conceptual framework to analyze user interactions based on mental representations. They also detail an expert system implementation using the ontology language OWL to express the semantics of the representations and the rule language SWRL to define the rule base for contextual reasoning. The chapter illustrates how an expert system can be used to guide users in an e-commerce setting by orchestrating a cognitive fit between the task environment and the task solution.