Michael Davern

Discovering potential and realizing value from information technology investments

Abstract: Information technology (IT) value has been measured at various levels of analysis, yet few authors would contend that the search for value has reached a point where practitioners and theoreticians are satisfied with its outcomes. We present a new perspective that emphasizes the importance of understanding where potential value lies and how best to relate it contextually to the measurement of the firm’s realized value across multiple levels of analysis. We develop the idea that complementary assets (especially business process design and human capital) influence the firm’s realization of value, using concepts such as locus of value and value conversion contingencies. Expanding beyond earlier process models of IT value, which begin with IT expenditure, our analysis of IT value emphasizes the consideration of potential value for an IT investment both in ex ante project selection, and ex post investment evaluation. We illustrate and validate the application of our framework using IT investments in a variety of business domains.

Measuring the effects of business intelligence systems: The relationship between business process and organizational performance

Business intelligence (BI) systems provide the ability to analyse business information in order to support and improve management decision making across a broad range of business activities. They leverage the large data infrastructure investments (e.g. ERP systems) made by firms, and have the potential to realise the substantial value locked up in a firms data resources. While substantial business investment in BI systems is continuing to accelerate, there is a complete absence of a specific and rigorous method to measure the realised business value, if any. By exploiting the lessons learned from prior attempts to measure business value of IT-intensive systems, we develop a new measure that is based on an understanding of the characteristics of BI systems in a process-oriented framework. We then employ the measure in an examination of the relationship between the business process performance and organizational performance, finding significant differences in the strength of the relationship between industry sectors. This study reinforces the need to consider the specific context of use when designing performance measurement for IT-intensive systems, and highlights the need for further research examining contextual moderators to the realisation of such performance benefits.

Transition to Adulthood: Delays and Unmet Needs among Adolescents and Young Adults with Asthma

OBJECTIVE To examine the effect of the transition to adulthood on financial and non-financial barriers to care in youth with asthma. STUDY DESIGN With National Health Interview Survey data from 2000 to 2005, we examined delays and unmet needs because of financial and non-financial barriers, evaluating the effect of adolescent (age, 12-17 years; n = 1539) versus young adult age (age, 18-24 years; N = 833), controlling for insurance, usual source of care, and sociodemographic characteristics. We also simulated the effects of providing public insurance to uninsured patients and a usual source of care to patients without one. RESULTS More young adults than adolescents encountered financial barriers resulting in delays (18.6% versus 8%, P < .05) and unmet needs (26.6% versus 11.4%, P < .05), although delays caused by non-financial barriers were similar (17.3% versus 14.9%, P = not significant). In logistic models young adults were more likely than adolescents to report delays (odds ratio [OR], 1.45; 95% CI, 1.02-2.08) and unmet needs (OR, 1.8; 95% CI, 1.29-2.52) caused by financial barriers. CONCLUSIONS Delays and unmet needs for care caused by financial reasons are significantly higher for young adults than they are for adolescents with asthma.

Nonresponse Rates are a Problematic Indicator of Nonresponse Bias in Survey Research

Survey researchers are rightly concerned with measuring the level of potential bias in estimates generated from the surveys.2 Bias in estimates can result from measurement error, processing/editing error, coverage error, and nonresponse error (Federal Committee on Statistical Methodology [FCSM] 2001). It is nonresponse bias that is the focus of this editorial and it is also the subject of the paper by Halbesleben and Whitman (2013) that this editorial accompanies.

Towards an integrated view of IT value measurement

One of the most enduring questions at the intersection of accounting and information systems is measuring the value of Information Technology (IT). This longevity is unsurprising — measuring the value of IT is difficult at best. Literature on this issue falls into two broad research streams. One stream, which draws largely on mainstream financial accounting and economics, employs independently observable measures, such as capital market reactions, return on assets and changes in market share, to assess the value of IT in an organization. The ready comparability of such measures makes them quite desirable. However, accurately linking them to the underlying IT is problematic as often they do not solely capture the impact of IT in the organization. An alternative stream of research, which draws on the behavioural sciences, uses more subjective, perceptual measures such as assimilation, user satisfaction, perceived net benefit, and perceptions and expectations of quality. These measures are often more closely connected to the underlying IT and are often more diagnostic with respect to how effectively IT is used and value is realized. Nevertheless as subjective measures they are open to all the biases and inconsistencies of human judgment. In this paper we present a framework for understanding the theoretical characteristics of independently observable and perceptual measures. We seek to provide a more integrated perspective on these otherwise disparate approaches to IT value measurement. Through a review of the generally accepted findings on IT value measurement, we establish the need for an integrated view and demonstrate how such an integrated perspective might operate and advance our understanding of IT value measurement.

The Effectiveness of an Interactive Small Group Diabetes Intervention in Improving Knowledge, Feeling of Control, and Behavior

The objective of this study is to evaluate the effectiveness of a small group intervention in improving knowledge, feeling of control, and behaviors related to self-management of diabetes. The intervention includes educational content on diabetes self-management as well as discussion of attitudes, feelings, and motivations about living with diabetes. The authors randomized volunteers into an intervention group that participated in the small-group learning activity and a control group that received a diabetes self-care book. A survey was conducted by telephone before and after each intervention and the difference in change over time between the groups was assessed for each outcome. Compared to the control group, participants in the small-group activity reported significant changes on all three outcomes adjusting for demographic differences between the groups. Because facilitating the learning session does not require clinically trained personnel, this type of intervention could broaden the resources available to people with diabetes.

The American Community Survey and Health Insurance Coverage Estimates: Possibilities and Challenges for Health Policy Researchers

OBJECTIVE To introduce the American Community Survey (ACS) and its measure of health insurance coverage to researchers and policy makers. DATA SOURCES/STUDY SETTING We compare the survey designs for the ACS and Current Population Survey (CPS) that measure insurance coverage. STUDY DESIGN We describe the ACS and how it will be useful to health policy researchers. PRINCIPAL FINDINGS Relative to the CPS, the ACS will provide more precise state and substate estimates of health insurance coverage at a point-in-time. Yet the ACS lacks the historical data and detailed state-specific coverage categories seen in the CPS. CONCLUSIONS The ACS will be a critical new resource for researchers. To use the new data to the best advantage, careful research will be needed to understand its strengths and weaknesses.

Estimating regression standard errors with data from the current population survey's public use file

This study examines whether reasonable standard errors for multivariate models can be calculated using the public use file of the Current Population Surveys Annual Social and Economic Supplement (CPS ASEC). We restrict our analysis to the 2003 CPS ASEC and model three dependent variables at the individual level: income, poverty, and health insurance coverage. We compare standard error estimates performed on the CPS ASEC public use file with those obtained from the Census Bureaus restricted internal data that include all the relevant sampling information needed to compute standard errors adjusted for the complex survey sample design. Our analysis shows that the multivariate standard error estimates derived from the public use CPS ASEC following our specification perform relatively well compared to the estimates derived from the internal Census Bureau file. However, it is essential that users of CPS ASEC data do not simply choose any available method since three of the methods commonly used for adjusting for the complex sample design produce substantially different estimates.

An Exploratory Model of Decision Quality and its Antecedents for Category Novices Using Multiple-Stage Shopping Engines

Since technology adopters are sensitive to perceived ease of use, online shoppers who are novices to a product category may be particularly sensitive. We developed a predictive model of decision quality centered on perceived ease of use for online shoppers who are novices to a product category. The antecedents of perceived ease of use in this study are novice category knowledge, perceived time pressure and purchase involvement. We had undergraduate students interact with multiple-stage shopping engines (MSSEs) of varying effort-accuracy tradeoffs in our purpose- built Web store. Both novice category knowledge and perceived time pressure negatively impacted purchase involvement and perceived ease of use of the MSSEs. Perceived ease of use of the MSSEs positively impacted their perceived usefulness. Perceived usefulness positively impacted decision quality, which was measured objectively, with data envelopment analysis, and subjectively, with the weighted additive method. Six of the seven hypotheses were supported. Surprisingly, there were no significant differences among the MSSEs and purchase involvement did not positively impact perceived ease of use. The results, analyzed via partial least squares, demonstrate the potential value of giving multiple-stage shopping engines to online shoppers who are novices to a product or service category.

Personalization and choice behavior: the role of personality traits

Online merchants use personalization technologies to gain knowledge of an individual customer and then generate preference-matched web content for the customer. Among the various types of personalization technologies, this research focuses on personalization engines that generate preference-matched content based on a customers prior transactions. Extant research in this area has focused on how to maximize knowledge mined from transaction logs to generate content that is highly similar to the customers past revealed preferences. However, it remains an empirical question as to whether the content closely matched with previous transactions is most likely to influence choice behavior. In this study, we postulate that the content closely matched with previous transactions may not be the most influential in biasing a customer. In the consideration and choice process, an individuals personality traits play a pivotal role in moderating the effect of personalized content. Drawing on research in marketing, we examine three key personality traits: need for cognition; variety seeking, and need for uniqueness; and explore their effects on choice behavior in the context of transaction-driven personalization. Research hypotheses are tested with over 2,000 pre-selected subjects in an online experiment based on a ringtone download website. We find that individual personality traits moderate content consideration and choice. Theoretical and practical implications of the findings are discussed.