James E. Yao

Bio: James E. Yao is an academic researcher from Montclair State University. The author has contributed to research in topics: Decision support system & Data warehouse. The author has an hindex of 10, co-authored 35 publications receiving 2389 citations. Previous affiliations of James E. Yao include University at Albany, SUNY & Texas A&M University–Commerce.

Personal innovativeness, social influences and adoption of wireless internet services via mobile technology

Abstract: Technology acceptance research has tended to focus on instrumental beliefs such as perceived usefulness and perceived ease of use as drivers of usage intentions, with technology characteristics as major external stimuli. Behavioral sciences and individual psychology, however, suggest that social influences and personal traits such as individual innovativeness are potentially important determinants of adoption as well, and may be a more important element in potential adopters' decisions. This paper models and tests these relationships in non-work settings among several latent constructs such as intention to adopt wireless mobile technology, social influences, and personal innovativeness. Structural equation analysis reveals strong causal relationships between the social influences, personal innovativeness and the perceptual beliefs—usefulness and ease of use, which in turn impact adoption intentions. The paper concludes with some important implications for both theory research and implementation strategies.

Technology acceptance model for wireless Internet

Abstract: Wireless Internet via mobile devices (WIMD) is leading the world into another spectrum of communications and means of conducting day‐to‐day business and life activities. Full bloom of wireless Internet services depends on user acceptance, as well as technology improvement. This paper develops a technology acceptance model for wireless Internet via mobile devices (TAM for wireless Internet), a conceptual framework to explain the factors influencing user acceptance of WIMD. By revising the technology acceptance model (TAM) to represent some unique features of the wireless system under study, TAM for wireless Internet proposes that constructs such as individual differences, technology complexity, facilitating conditions, social influences, and wireless trust environment determine user‐perceived short and long‐term usefulness, and ease of using WIMD. These, in turn, determine user intention and willingness to adopt WIMD. Twelve propositions are developed to promote and facilitate future empirical research relating to WIMD.

Exploring Factors Associated with Wireless Internet via Mobile Technology Acceptance in Mainland China

Abstract: This study explores factors significantly impact the acceptance of Wireless Internet via Mobile Technology (WIMT) in China. The results indicate that the acceptance of WIMT is related with factors of: perceived usefulness, perceived ease of use, social influences, wireless trust environment, and facilitating conditions. It provides diagnostic insight into how different factors influence user intention to accept WIMT in China, and thus help business build solid strategy to prompt WIMT and m-commerce there. INTRODUCTION Mobile Commerce (also called m-commerce or wireless commerce) represents the convergence of two technologies – the web, which has radically changed how to conduct business, and wireless technology, which through mobile devices such as cell phone, PDA, or pager, has added a mobile dimension to e-commerce and mobile computing (Coyle, 2001). Mobile Commerce is a subset of electronic commerce (e-commerce), which is forecasted to continue to grow and has a profound impact on the global business environment despite recent poor performance from the Internet sector (Forrest Research, 2001). Indeed, m-commerce, which delivers e-commerce capabilities directly into the consumer's hand via wireless technology, can be a force with strong

Data Mining Software

Abstract: With increasing amounts of data being generated by businesses and researchers, there is a need for fast, accurate, and robust algorithms for data analysis. Improvements in databases technology, computing performance, and artificial intelligence have contributed to the development of intelligent data analysis. The primary aim of data mining is to discover patterns in the data that lead to better understanding of the data generating process and to make useful predictions (Hand, Mannila, & Smyth, 2001). Most companies now collect and refine massive quantities of data in data warehouses. These companies realize that to succeed in a fast paced world, business users need to be able to get information on demand. Many organizations now view information as one of their most valuable assets, and data mining software allows a company to make full use of these information assets. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning and neural networks, decision trees, naive-Bayes, K-nearest neighbor, rule induction, clustering, rules based, linear and logistical regression time sequence, and so forth (Wang, 2005). There is never enough time to think of all the important questions; that is why the computer should do this itself. It can provide the winning edge in business by exploring the database and it brings back invaluable information.

Organizational Size: A Significant Predictor of it Innovation Adoption

Abstract: Companies or organizations have to constantly adopt new technological innovations one way or the other in order to improve or keep their efficiency or competitive advantages in business. The presen...

Consumer acceptance of online banking: an extension of the technology acceptance model

Abstract: Advances in electronic banking technology have created novel ways of handling daily banking affairs, especially via the online banking channel. The acceptance of online banking services has been rapid in many parts of the world, and in the leading e‐banking countries the number of e‐banking contracts has exceeded 50 percent. Investigates online banking acceptance in the light of the traditional technology acceptance model (TAM), which is leveraged into the online environment. On the basis of a focus group interview with banking professionals, TAM literature and e‐banking studies, we develop a model indicating online‐banking acceptance among private banking customers in Finland. The model was tested with a survey sample (n=268). The findings of the study indicate that perceived usefulness and information on online banking on the Web site were the main factors influencing online‐banking acceptance.

Personal innovativeness, social influences and adoption of wireless internet services via mobile technology

Abstract: Technology acceptance research has tended to focus on instrumental beliefs such as perceived usefulness and perceived ease of use as drivers of usage intentions, with technology characteristics as major external stimuli. Behavioral sciences and individual psychology, however, suggest that social influences and personal traits such as individual innovativeness are potentially important determinants of adoption as well, and may be a more important element in potential adopters' decisions. This paper models and tests these relationships in non-work settings among several latent constructs such as intention to adopt wireless mobile technology, social influences, and personal innovativeness. Structural equation analysis reveals strong causal relationships between the social influences, personal innovativeness and the perceptual beliefs—usefulness and ease of use, which in turn impact adoption intentions. The paper concludes with some important implications for both theory research and implementation strategies.

Technology acceptance model: a literature review from 1986 to 2013

Abstract: With the ever-increasing development of technology and its integration into users' private and professional life, a decision regarding its acceptance or rejection still remains an open question. A respectable amount of work dealing with the technology acceptance model (TAM), from its first appearance more than a quarter of a century ago, clearly indicates a popularity of the model in the field of technology acceptance. Originated in the psychological theory of reasoned action and theory of planned behavior, TAM has evolved to become a key model in understanding predictors of human behavior toward potential acceptance or rejection of the technology. The main aim of the paper is to provide an up-to-date, well-researched resource of past and current references to TAM-related literature and to identify possible directions for future TAM research. The paper presents a comprehensive concept-centric literature review of the TAM, from 1986 onwards. According to a designed methodology, 85 scientific publications have been selected and classified according to their aim and content into three categories such as (i) TAM literature reviews, (ii) development and extension of TAM, and (iii) modification and application of TAM. Despite a continuous progress in revealing new factors with significant influence on TAM's core variables, there are still many unexplored areas of model potential application that could contribute to its predictive validity. Consequently, four possible future directions for TAM research based on the conducted literature review and analysis are identified and presented.

The use of a decomposed theory of planned behavior to study Internet banking in Taiwan

Abstract: With the liberalization and internalization of financial markets, in terms of the entrance of the World Trade Organization, banks in Taiwan face pressures in service quality and administrative efficiency. Predicting customers’ intention to adopt Internet banking is an important issue. Attempts to understand how an individual's belief, embracing attitude, subjective norm and perceived behavioral control, can influence intention. Two versions of the model of the theory of planned behavior (TPB) – pure and decomposed – are examined and compared to the theory of reasoned action (TRA). Data are collected from approximately 425 respondents and structural equation modeling is used to analyze the responses. Results generally support TRA and TPB and provide a good fit to the data.