Measuring the Return on Information Technology: A Knowledge-Based Approach for Revenue Allocation at the Process and Firm Level

This paper proposes an approach for measuring the return on Information Technology (IT) investments. A review of existing methods suggests the difficulty in adequately measuring the returns of IT at various levels of analysis (e.g., firm or process level). To address this issue, this study aims to develop a method for allocating the revenue and cost of IT initiatives at any level of analysis using a common unit of measurement. Following the knowledge-based view (KBV), this paper proposes an analytic method for measuring the historical revenue and cost of IT investments by estimating the amount of knowledge necessary to generate a common unit of output from any business process. The amount of required knowledge is operationalized using the ¡®average learning time\u27 measure. The proposed operationalization is illustrated with a practical case example. The proposed KBV approach is extended specifically for IT resources, allowing us to assess the Return on IT (ROIT) using a typical productivity ratio (similar to ROI or ROA) that accurately captures the true business value of IT (despite any complementarities) at virtually any level of analysis

Time-dependent transport through a T-coupled quantum dot

We are considering the time-dependent transport through a discrete system, consiting of a quantum dot T-coupled to an infinite tight-binding chain. The periodic driving that is induced on the coupling between the dot and the chain, leads to the emergence of a characteristic multiple Fano resonant profile in the transmission spectrum. We focus on investigating the underlying physical mechanisms that give rise to the quantum resonances. To this end, we use Floquet theory for calculating the transmission spectrum and in addition employ the Geometric Phase Propagator (GPP) approach [Ann. Phys. 375, 351 (2016)] to calculate the transition amplitudes of the time-resolved virtual processes, in terms of which we describe the resonant behavior. This two fold approach, allows us to give a rigorous definition of a quantum resonance in the context of driven systems and explains the emergence of the characteristic Fano profile in the transmission spectrum.Comment: 9 pages, 4 figure

Tutorial on Latent Growth Models for Longitudinal Data Analysis

This tutorial introduces Latent Growth Modeling (LGM) as a promising new method for analyzing longitudinal data when interested in understanding the process of change over time. Given the need to go beyond cross-sectional models in IS research, explore complex longitudinal IS phenomena, and test Information Systems (IS) theories over time, LGM is proposed as a complementary method to help IS researchers propose time-dependent hypotheses and make longitudinal inferences about IS theories. The tutorial leader will explain the importance of theorizing patterns of change over time, how to propose longitudinal hypotheses, and how LGM can help test such hypotheses. All three tutorial facilitators will describe the tenets of LGM and offer guidelines for applying LGM in IS research including framing time-dependent hypotheses that can be readily tested with LGM. The three tutorial facilitators will also explain how to use LGM in SAS 9.2 with a hands-on application that will attempt to model the complex longitudinal relationship between IT and firm performance using longitudinal data from Fortune 1000 firms. The tutorial facilitators will also draw comparisons with other existing methods for modeling longitudinal data and they will also discuss the advantages and disadvantages of LGM for identifying longitudinal patterns in data

FIT DOES MATTER! AN EMPIRICAL STUDY ON PRODUCT FIT UNCERTAINTY IN ONLINE MARKETPLACES

This paper examines the antecedents and consequences of product uncertainty in online marketplaces by conceptualizing the dimensions of product uncertainty - description uncertainty (identifying product characteristics), performance uncertainty (inferring product‟s future performance) and fit uncertainty (matching product‟s characteristics with buyer‟s needs), with the focus on product fit uncertainty. It also theorizes the distinction, relationship, and effects of the three dimensions of product uncertainty. Finally, it proposes a set of IT artifacts to reduce product fit uncertainty. The hypotheses are tested with survey and website transaction data from 274 buyers in Taobao, the largest online marketplace in China. The results first demonstrate the distinction between three dimensions of product uncertainty, show that relative to description and performance uncertainty, only fit uncertainty has significant effect on price premiums, satisfaction, product returns, and repurchase intentions, and support the effects of the use of IT artifacts, such as instant messenger, product forums, and decision support tools on reducing fit uncertainty. Implications for research, theory and practice are discussed

Does Online Reputation Matter? An Empirical Investigation of Reputation and Trust in Online Auction Markets

Trust is an essential component of buyer-seller relationships, especially for online transactions. Appropriate feedback mechanisms help buyers build trust towards reputable sellers. Drawing from sociology and economics, we show that buyers pay a price premium to transact with reputable sellers, especially for expensive products. To empirically examine the relationship between feedback and price premiums, we collected data for 19 products from 702 completed online auctions from the auction site of ebay.com (www.ebay.com). Results showed a significant correlation between feedback and price premiums for all products. This correlation became increasingly significant for more expensive products. This paper contributes to a better understanding of the value of reputation and trust in EC

Latent Growth Modeling in IS Research: Basic Tenets, Illustration, and Practical Guidelines

This paper introduces Latent Growth Modeling (LGM) as a feasible method for analyzing longitudinal data to understand the process of change over time. Given the need to go beyond cross-sectional models, explore longitudinal Information Systems (IS) phenomena, and test IS theories over time, LGM is proposed as a complementary method to help IS researchers propose and evaluate time-centric hypotheses and make longitudinal inferences. The paper first describes the basic tenets of LGM and offers guidelines for using LGM in IS research, including framing hypotheses with time as a central component and implementing LGM models to test these hypotheses. The application of LGM in IS research is illustrated by modeling the longitudinal relationship between two IT variables (IT infrastructure and IT labor) and firm performance with 2001-2004 data from Fortune 1000 firms. Comparisons with other methods for analyzing longitudinal data reveal the advantages of LGM for studying time-dependent relationships and growth patterns

Does the Adoption of EMR Systems Inflate Medicare Reimbursements?

The adoption of EMR systems has been argued to lead to physicians “upcoding” their patients to inflate insurance reimbursements. In this paper, we examine if the adoption of the Clinical Physician Order Entry (CPOE) system is associated with an increase in the complexity of the patients\u27 case mix that hospitals report (termed upcoding ). We make use of a staggered roll-out of the Recovery Audit Program to combat upcoding as a natural experiment to assess the impact of the adoption of the CPOE systems on the case mix that a hospital reports. We find that on average the adoption of CPOE systems is associated with an increase in the reported case mix of hospitals, and that the Audit program has had an effect on reducing the case mix that hospitals report to Medicare for reimbursement. Implications for preventing inflated reimbursements due to upcoding are discussed