Adaptive System Use; An Investigation at the System Feature Level

System use has been simply defined and measured. In this research, we investigate the dynamics of system use at the individual level. A new concept called adaptive system use is developed to capture user modifications to their use of system features. A causal model including antecedents of adaptive system use is developed. Three antecedents of adaptive system use are identified. Using a sample of 282 users of MS Office, our study examines the psychometric properties of adaptive system use and confirms the research model. Four types or dimensions of adaptive system use are trying new features, feature substitution, feature combination, and feature repurposing. Triggers are found to be the most important antecedent of adaptive system use. Facilitating conditions also affect adaptive system use significantly. Research and practical implications are offered

Causal Relationships between Perceived Enjoyment and Perceived Ease of Use: An Alternative Approach

Identifying causal relationships is an important aspect of scientific inquiry. Causal relationships help us to infer, predict, and plan. This research investigates the causal relationships between two constructs, perceived enjoyment (PE) and perceived ease of use (PEOU), within the nomological net of user technology acceptance. PE has been theorized and empirically validated as either an antecedent or a consequence of PEOU. We believe that there are two reasons that account for this ambiguity the conceptual coupling of PE and PEOU and the limitations of covariance-based statistical methods. Accordingly, we approach this inconsistency by providing more theoretical reasoning and employing an alternative statistical method, namely Cohen\u27s path analysis. Specifically, as suggested by previous research on the difference between utilitarian and hedonic systems, we propose the conditional dominance of causal directions. Empirical results from two studies using different technologies and user samples support the theoretical claim that the PE?PEOU causal direction outweighs the PEOU?PE direction for utilitarian systems. There are both theoretical and the methodical contributions of this research. The approach applied in this research can be generalized to study causal relationships between conceptually coupled variables, which otherwise may be overlooked by confirmatory methods. We encourage researchers to pay attention to causal directions in addition to causal connectedness

A Research Agenda Toward a Better Conceptualization of IT Use

IT use is a primary concept that should be clarified carefully since it is a major concept in several existing IS theoretical models such as the Technology Acceptance Model, Task-Technology Fit, and IS Success model. Unfortunately, most studies define this concept too simply. This can constrain our understandings of IT impacts. Therefore, it is necessary to conceptualize IT use and explore its multiple dimensions. This research attempts to formulate a research agenda for this purpose. Using activity theory and other related theories, we propose a research framework to conceptualize IT use and a multi-item scale to measure it

An Empirical Study of the Roles of Affective Variables in User Adoption of Search Engines

The current study is built upon prior research and is an attempt to explore the roles of affective variables in user technology adoption. Two different affective variables, computer playfulness and perceived enjoyment, were examined and their relationships with each other and with cognitive and behavioral variables were hypothesized. An empirical study using survey method was conducted. Analyses with the PLS technique confirmed most of the hypotheses. Our findings suggest that perceived enjoyment has a significant impact on perceived ease of use, but no direct effect on behavioral intention. Perceived enjoyment mediates the impact of computer playfulness on PEOU, which has not been studied before

ONLINE SELLERS\u27 TRUST AND USE OF ONLINE AUCTION MARKETPLACES

More and more people sell things online and trust is an important factor in online selling. This research is aimed at understanding the roles of trust in online sellers\u27 continued use of online auction marketplaces. Given the uniqueness of online auction practice, we identify the need for differentiating sellers\u27 trust in the intermediary and in buyers. A balanced view of cognitive and affective trust is incorporated with the Motivational Model of technology acceptance to predict sellers\u27 use of online auction marketplaces. Empirical data collected from online auction sellers in uBid.com confirmed our model. Specifically, our findings show that, for online auction sellers, (1) trust has both cognitive and affective components; (2) trust in the intermediary (e.g., eBay.com) impacts trust in the community of buyers through the trust transference mechanism; (3) trusting attitudes antecede user acceptance and use factors including perceived usefulness and perceived enjoyment, which in turn influence sellers\u27 intention to return;; and (4) perceived enjoyment is an important antecedent of sellers\u27 retention. Besides theoretical contributions, this research also has practical implications

Adaptive IT Use: Conceptualization and Measurement

IT use is an important concept both in research and in practice. Yet, IT use has been simply defined and measured in IS research. Presently used measurements do not reflect the dynamics of users’ IT use behavior, which are important and account for job performance. This research aims at conceptualizing a new construct to capture the changes in IT use and developing an instrument for it. From an adaptive structuration perspective, we propose a new construct named Adaptive IT Use (AITU) to capture use changes in both IT feature set (size, content, and network), and the spirit of IT features. We further propose six dimensions of AITU and corresponding measuring items. After interviews and card-sorting experiments, an instrument of AITU is developed

Applying Markus and Robey\u27s Causal Structure to Examine User Technology Acceptance Research: A New Approach

In this paper, we examine prior research on user technology acceptance from the perspective of theoretical structures based on Markus and Robey’s causal structure. Prior studies usually take a technology imperative perspective, use variance theories, and emphasize the micro level of analysis. We argue that this combination is limited. This may lead to some inconsistencies and limited explanatory powers in the existing studies. We propose an alternative “emergent perspective – process theories – mixed level of analysis” approach to study technology acceptance phenomena. To demonstrate how the new approach can be used to guide research, a new research model is proposed and several propositions are derived and discussed. This study draws on several prior theories and models but reassembles them in a novel way. The paper concludes with implications for both research and practice

An Empirical Study on Causal Relationships between Perceived Enjoyment and Perceived Ease of Use

Causality is critical for our understanding of user technology acceptance. However, findings regarding the causal relationship between perceived enjoyment (PE) and perceived ease of use (PEOU) are not conclusive. PE has been theorized and empirically validated as either an antecedent or a consequence of PEOU. Covariance-based methods such as the widely used Structural Equation Modeling (SEM), albeit robust in examining causal connectedness, are limited in detecting causal direction and therefore cannot provide additional evidence for one view or the other. This study provides an alternative statistical method, Cohen’s path analysis to explore causal relationship. Empirical results from two studies support that the PE&#;PEOU causal direction is stronger than the PEOU&#;PE direction for utilitarian systems

MOON: MapReduce On Opportunistic eNvironments

Abstract—MapReduce offers a flexible programming model for processing and generating large data sets on dedicated resources, where only a small fraction of such resources are every unavailable at any given time. In contrast, when MapReduce is run on volunteer computing systems, which opportunistically harness idle desktop computers via frameworks like Condor, it results in poor performance due to the volatility of the resources, in particular, the high rate of node unavailability. Specifically, the data and task replication scheme adopted by existing MapReduce implementations is woefully inadequate for resources with high unavailability. To address this, we propose MOON, short for MapReduce On Opportunistic eNvironments. MOON extends Hadoop, an open-source implementation of MapReduce, with adaptive task and data scheduling algorithms in order to offer reliable MapReduce services on a hybrid resource architecture, where volunteer computing systems are supplemented by a small set of dedicated nodes. The adaptive task and data scheduling algorithms in MOON distinguish between (1) different types of MapReduce data and (2) different types of node outages in order to strategically place tasks and data on both volatile and dedicated nodes. Our tests demonstrate that MOON can deliver a 3-fold performance improvement to Hadoop in volatile, volunteer computing environments