Technology Use and Performance: A Field Study of Broker Workstations*

Organizations invest in technology with the expectation that it will contribute to performance, and members of the organization must use technology for it to make a contribution. For this reason, it is important for managers and designers to understand and predict system use. This paper develops a model of workstation use in a field setting where the use of the system is an integral part of the user's job. The model is based on the Technology Acceptance Model (TAM), which we extended to include social norms, user performance, and two control variables. Brokers and sales assistants in the privateclient group of a major investment bank provided data to test our extended model. The core perception variables in TAM do not predict use in this study. Social norms and one's job requirements are more important in predicting use than workers' perceptions about ease of use and usefulness. The paper discusses the implications of these findings and suggests directions for future research.     

The Effect of Perceived Novelty on the Adoption of Information Technology Innovations: A Risk/Reward Perspective

A fundamental characteristic of any innovation is its novelty, the newness or freshness of the innovation in the eyes of the adopter. Past research has often considered novelty to be inherent to an information technology (IT) innovation, yet it is also likely that perceptions of novelty differ widely across individuals. Nevertheless, the role that the novelty of an IT innovation plays in adoption is not well understood. The primary goal of this research effort is to frame the perceived novelty of an IT innovation as a salient affective belief in the nomological network related to adoption. Further, we examine how perceived novelty influences the way individuals reconcile their perceptions of risk versus reward when considering the adoption of an IT innovation. Two empirical studies with 424 and 138 participants, respectively, examine the effect of perceived novelty on IT innovations from a risk/reward perspective. Results indicate that perceived novelty is a salient affective belief that plays a significant role in the adoption of IT innovations. Implications for both theory and organizational decision making are examined.     

Impacts of Information and Communication Technology Implementations on Employees' Jobs in Service Organizations in India: A Multi‐Method Longitudinal Field Study

India is an important frontier for economic growth, investments, and development. The service sector, like the manufacturing industry, in India is booming. Following the trend of their western counterparts, service organizations in India are implementing enterprise‐level information and communication technologies (ICTs) to support service processes. In this paper, we used socio‐technical systems theory to develop hypotheses about the effects of ICTs on the five job characteristics, i.e., skill variety, task identity, task significance, autonomy, and feedback, in the job characteristics model (JCM) in a service organization (a bank) in India. We also tested the entire JCM that relates job characteristics to job satisfaction and job performance via various mediators and moderators. In a 32‐month longitudinal field study of 1743 employees, we gathered one wave of data before an ICT implementation and two waves after the implementation. We found that, although the ICT enriched employees' job characteristics, employees reported significantly lower job satisfaction and job performance. To understand this puzzling finding, we conducted a qualitative study and identified four contextual forces that contribute to these results and hinder successful implementation of ICTs in the service sector in India and, possibly, other developing countries: environmental barriers, learning difficulty, culture shock, and employee valuation.     

A Tiered Framework for Risk‐Relevant Characterization and Ranking of Chemical Exposures: Applications to the National Children's Study (NCS)

A challenge for large‐scale environmental health investigations such as the National Children's Study (NCS), is characterizing exposures to multiple, co‐occurring chemical agents with varying spatiotemporal concentrations and consequences modulated by biochemical, physiological, behavioral, socioeconomic, and environmental factors. Such investigations can benefit from systematic retrieval, analysis, and integration of diverse extant information on both contaminant patterns and exposure‐relevant factors. This requires development, evaluation, and deployment of informatics methods that support flexible access and analysis of multiattribute data across multiple spatiotemporal scales. A new “Tiered Exposure Ranking” (TiER) framework, developed to support various aspects of risk‐relevant exposure characterization, is described here, with examples demonstrating its application to the NCS. TiER utilizes advances in informatics computational methods, extant database content and availability, and integrative environmental/exposure/biological modeling to support both “discovery‐driven” and “hypothesis‐driven” analyses. “Tier 1” applications focus on “exposomic” pattern recognition for extracting information from multidimensional data sets, whereas second and higher tier applications utilize mechanistic models to develop risk‐relevant exposure metrics for populations and individuals. In this article, “tier 1” applications of TiER explore identification of potentially causative associations among risk factors, for prioritizing further studies, by considering publicly available demographic/socioeconomic, behavioral, and environmental data in relation to two health endpoints (preterm birth and low birth weight). A “tier 2” application develops estimates of pollutant mixture inhalation exposure indices for NCS counties, formulated to support risk characterization for these endpoints. Applications of TiER demonstrate the feasibility of developing risk‐relevant exposure characterizations for pollutants using extant environmental and demographic/socioeconomic data.