Expectation confirmation research in general, and in information systems (IS) in particular, has produced conflicting results. In this paper, we discuss six different models of expectation confirmation: assimilation, contrast, generalized negativity, assimilation-contrast, experiences only, and expectations only. Relying on key constructs from the technology acceptance model (TAM), we test each of these six models that suggests different roles for expectations and experiences of the key predictor—here, perceived usefulness—and their impacts on key outcomes—here, behavioral intention, use, and satisfaction. Data were collected in a field study from 1,113 participants at two points in time. Using polynomial modeling and response surface analysis, we provide the analytical representations for each of the six models and empirically test them to demonstrate that the assimilation-contrast is the best existing model in terms of its ability to explain the relationships between expectations and experiences of perceived usefulness and important dependent variables—namely, behavioral intention, use, and satisfaction—in individual-level research on IS implementations.
Mixed methods research is an approach that combines quantitative and qualitative research methods in the same research inquiry. Such work can help develop rich insights into various phenomena of interest that cannot be fully understood using only a quantitative or a qualitative method. Notwithstanding the benefits and repeated calls for such work, there is a dearth of mixed methods research in information systems. Building on the literature on recent methodological advances in mixed methods research, we develop a set of guidelines for conducting mixed methods research in IS. We particularly elaborate on three important aspects of conducting mixed methods research: (1) appropriateness of a mixed methods approach; (2) development of meta-inferences (i.e., substantive theory) from mixed methods research; and (3) assessment of the quality of meta-inferences (i.e., validation of mixed methods research). The applicability of these guidelines is illustrated using two published IS papers that used mixed methods.
We propose a model to study expectation confirmation in information systems. The proposed model is based on the assimilation-contrast model and prospect theory, and suggests that both are needed to account for the magnitude and direction of the deviations between experiences and expectations. Using the technology acceptance model's (TAM) primary construct-namely, perceived usefulness-expectations and experiences were conceptualized and operationalized to test our model. Data were collected in a field study from 1,113 participants at two points in time. Using polynomial modeling and response surface analysis, we demonstrated that our model offers a good explanation of the relationship among information systems expectations, experiences, and use. We discuss theoretical and practical implications.
Effective search support is an important tool for helping individuals deal with the problem of information overload. This is particularly true in the field of nanotechnology, where information from patents, grants, and research papers is growing rapidly. Guided by cognitive fit and cognitive load theories, we develop an advanced Web-based system, Nano Mapper, to support users' search and analysis of nanotechnology developments. We perform controlled experiments to evaluate the functions of Nano Mapper. We examine users' search effectiveness, efficiency, and evaluations of system usefulness, ease of use, and satisfaction. Our results demonstrate that Nano Mapper enables more effective and efficient searching, and users consider it to be more useful and easier to use than the benchmark systems. Users are also more satisfied with Nano Mapper and have higher intention to use it in the future. User evaluations of the analysis functions are equally positive.
Increasing global connectivity makes emerging infectious diseases (EID) more threatening than ever before. Various information systems (IS) projects have been undertaken to enhance public health capacity for detecting EID in a timely manner and disseminating important public health information to concerned parties. While those initiatives seemed to offer promising solutions, public health researchers and practitioners raised concerns about their overall effectiveness. In this paper, we argue that the concerns about current public health IS projects are partially rooted in the lack of a comprehensive framework that captures the complexity of EID management to inform and evaluate the development of public health IS. We leverage loose coupling to analyze news coverage and contact tracing data from 479 patients associated with the severe acute respiratory syndrome (SARS) outbreak in Taiwan. From this analysis, we develop a framework for outbreak management. Our proposed framework identifies two types of causal circles—coupling and decoupling circles—between the central public health administration and the local capacity for detecting unusual patient cases. These two circles are triggered by important information-centric activities in public health practices and can have significant influence on the effectiveness of EID management. We derive seven design guidelines from the framework and our analysis of the SARS outbreak in Taiwan to inform the development of public health IS. We leverage the guidelines to evaluate current public health initiatives. By doing so, we identify limitations of existing public health IS, highlight the direction future development should consider, and discuss implications for research and public health policy.
The paper presents a model integrating theories from collaboration research (i.e., social presence theory, channel expansion theory, and the task closure model) with a recent theory from technology adoption research (i.e., unified theory of acceptance and use of technology, abbreviated to UTAUT) to explain the adoption and use of collaboration technology. We theorize that collaboration technology characteristics, individual and group characteristics, task characteristics, and situational characteristics are predictors of performance expectancy, effort expectancy, social influence, and facilitating conditions in UTAUT. We further theorize that the UTAUT constructs, in concert with gender, age, and experience, predict intention to use a collaboration technology, which in turn predicts use. We conducted two field studies in Finland among (1) 349 short message service (SMS) users and (2) 447 employees who were potential users of a new collaboration technology in an organization. Our model was supported in both studies. The current work contributes to research by developing and testing a technology-specific model of adoption in the collaboration context.
Employees' underutilization of new information systems undermines organizations' efforts to gain benefits from such systems. The two main predictors of individual-level system use in prior research--behavioral intention and facilitating conditions--have limitations that we discuss. We introduce behavioral expectation as a predictor that addresses some of the key limitations and provides a better understanding of system use. System use is examined in terms of three key conceptualizations: duration, frequency, and intensity. We develop a model that employs behavioral intention, facilitating conditions, and behavioral expectation as predictors of the three conceptualizations of system use. We argue that each of these three determinants play different roles in predicting each of the three conceptualizations of system use. We test the proposed model in the context of a longitudinal field study of 321 users of a new information system. The model explains 65 percent, 60 percent, and 60 percent of the variance in duration, frequency, and intensity of system use respectively. We offer theoretical and practical implications for our findings.
Individual adoption of technology has been studied extensively in the workplace. Far less attention has been paid to adoption of technology in the household. In this paper, we performed the first quantitative test of the recently developed model of adoption of technology in households (MATH). Further, we proposed and tested a theoretical extension of MATH by arguing that key demographic characteristics that vary across different life cycle stages would play moderating roles. Survey responses were collected from 746 U.S. households that had not yet adopted a personal computer. The results showed that the integrated model, including MATH constructs and life cycle characteristics, explained 74 percent of the variance in intention to adopt a PC for home use, a significant increase over baseline MATH that explained 50 percent of the variance. Finally, we compared the importance of various factors across household life cycle stages and gained a more refined understanding of the moderating role of household life cycle stage.
While technology adoption in the workplace has been studied extensively, drivers of adoption in homes have been largely overlooked. This paper presents the results of a nation-wide, two-wave, longitudinal investigation of the factors driving personal computer (PC) adoption in American homes. The findings revealed that the decisions driving adoption and non-adoption were significantly different. Adopters were driven by utilitarian outcomes, hedonic outcomes (i.e., fun), and social outcomes (i.e., status) from adoption. Non- adopters, on the other hand, were influenced primarily by rapid changes in technology and the consequent fear of obsolescence. A second wave of data collection conducted six months after the initial survey indicated an asymmetrical relationship between intent and behavior, with those who did not intend to adopt a PC following more closely with their intent than those who intended to adopt one. We present important implications for research on adoption of technologies in homes and the workplace, and also discuss challenges facing the PC industry.