Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empirically compare the eight models and their extensions, (3) formulate a unified model that integrates elements across the eight models, and (4) empirically validate the unified model. The eight models reviewed are the theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, the model of PC utilization, the innovation diffusion theory, and the social cognitive theory. Using data from four organizations over a six-month period with three points of measurement, the eight models explained between 17 percent and 53 percent of the variance in user intentions to use information technology. Next, a unified model, called the Unified Theory of Acceptance and Use of Technology (UTAUT), was formulated, with four core determinants of intention and usage, and up to four moderators of key relationships. UTAUT was then tested using the original data and found to outperform the eight individual models (adjusted R2 of 69 percent). UTAUT was then confirmed with data from two new organizations with similar results (adjusted R2 of 70 percent). UTAUT thus provides a useful tool for managers needing to assess the likelihood of success for new technology introductions and helps them understand the drivers of acceptance in order to proactively design interventions (including training, marketing, etc.) targeted at populations of users that may be less inclined to adopt and use new systems. The paper also makes several recommendations for future research including developing a deeper understanding of the dynamic influences studied here, refining measurement of the core constructs used in UTAUT, and understanding the organizational outcomes associated with new technology use.

User acceptance of information technology: toward a unified view

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

计量经济分析 = Econometric analysis

Attachment--and loss.

To examine the essential determinants of green purchasing by multinational corporations� (MNC) subsidiaries, this study takes institutional theory as a foundation and focuses on the institutional duality associated with localization and globalization. Specifically, we develop a model to explain subsidiaries� green purchasing and empirically test the model with data from 141 purchasing managers and senior purchasing staff members from subsidiaries in 39 countries. Our results suggest that pressures from headquarters and the local environment do not affect subsidiaries� green purchasing directly; rather, they exert indirect influences through local tailoring. This study contributes to extant literature by revealing the significance of local tailoring in an MNC context. In addition, our findings offer several implications for practice by providing a roadmap for disseminating green purchasing across the subsidiaries of an MNC, as well as highlighting the importance of both clear communication about the benefits of green purchasing and internal audits.

Green Purchasing by MNC Subsidiaries: The Role of Local Tailoring in the Presence of Institutional Duality

A successful enterprise resource planning (ERP) system ultimately requires loyal use—proactive, extended use and willingness to recommend such uses to others—by employees. Building on interactional psychology literature and situational strength theory, we emphasize the importance of psychological commitment, in addition to behavioral manifestation, in a multilevel model of loyal use. Our empirical test of the model uses data from 485 employees and 166 information system professionals in 47 large Taiwanese organizations. Individual-level analyses suggest that perceived benefits and workload partially mediate the effects of perceived information quality (IQ) and system quality (SQ) on loyal use. Cross-level analyses show that IQ at the organizational level alleviates the negative effect of an employee’s perceived workload on loyal use; organization-level SQ and service-oriented organizational citizenship behaviors (SOCBs) of internal information systems staff reduce the influence of employees’ perce...

A Multilevel Approach to Examine Employees’ Loyal Use of ERP Systems in Organizations

The growth of Web 2.0 has produced enormous amounts of user-generated content that contains important information about individuals' attitudes, perceptions, and opinions toward products, social events, and political issues. The volume of such content is increasing exponentially, making its search, analysis, and use more difficult and thus favoring advanced tools that aid in information search and processing. We propose an integrated framework that offers an infrastructure necessary for accessing, integrating, and analyzing multilingual user-generated content from different social media sites. Building on this framework, we develop the Dark Web Forum Portal (DWFP) that supports the gathering and analyses of social media content concerning security. Our evaluation results show that users supported by DWFP complete tasks better and faster than those using the benchmark forum. Participants consider DWFP to be better in terms of system quality, usefulness, ease of use, satisfaction and intention to use.

An integrated framework for analyzing multilingual content in Web 2.0 social media

Top persuaders in a social network are social entities whose adoption of a product or service will result in the largest numbers of other entities in the network adopting the same product or service. Predicting top persuaders is critical to an expanding array of important social network-centric applications, such as viral marketing, customer retention, and political message promotion. This study formulates the top persuader prediction problem and develops a novel method to predict top persuaders. Our method development is rooted in eminent social network theories that reveal several forces central to social persuasion, including social influence, entity similarity, and structural equivalence. Our method innovatively integrates these forces to predict top persuaders in a social network, in contrast to existing methods that primarily focus on social influence. Specifically, we introduce persuasion probability that denotes the likelihood of persuasion conditioned on these forces. We then propose how to estimate persuasion probabilities, develop an algorithm to predict top persuaders using the estimated persuasion probabilities, and analyze the theoretical property of the algorithm. We conduct an extensive evaluation with real-world social network data and show that our method substantially outperforms prevalent methods from representative previous research and salient industry practices.

Top Persuader Prediction for Social Networks

Retrieving a patient's prior examination images that are relevant to the current ones is a critical component in radiologists' primary examination reading services. The important role of such image retrieval support will be greatly accentuated in digital radiology practice. Radiologists' knowledge of patient prior image retrievals is rooted in their interpretation and application of the pertinent underlying medical/radiological knowledge as well as in their clinical training and experiences. At the same time, this knowledge may vary with individual practice preferences and styles, and may dynamically evolve over time. The complexity and dynamics suggest that patient image retrievals are a promising area for artificial intelligence-based automated learning techniques. Automated learning of patient image retrieval knowledge can provide continuous knowledge repository update support in an image retrieval knowledge-based system. However, the implementation of the learning techniques needs to address several challenges that include missing and noisy data, as well as multiple decision outcomes. Two techniques based on salient automated learning paradigms, neural network and symbolic learning, are investigated. Specifically, we describe the design or extension of each learning technique to address the unique characteristics of patient image retrieval knowledge and compare the resulting learning performances. The results show that the knowledge derived from the automated learning methods can achieve effective image retrievals that are comparable to those based on a knowledge-engineer-driven approach.

Paul Jen-Hwa Hu

Papers

Green Purchasing by MNC Subsidiaries: The Role of Local Tailoring in the Presence of Institutional Duality

A Multilevel Approach to Examine Employees’ Loyal Use of ERP Systems in Organizations

An integrated framework for analyzing multilingual content in Web 2.0 social media

Top Persuader Prediction for Social Networks

Automated learning of patient image retrieval knowledge: neural networks versus inductive decision trees