Management Faculty Research and Publications

Does information technology investment influences firm’s market value? The case of non-publicly traded healthcare firms

Rajiv Kohli et al. — Tue, 30 Apr 2013 14:46:27 PDT

Managers make informed information technology investment decisions when they are able to quantify how IT contributes to firm performance. While financial accounting measures inform IT’s influence on retrospective firm performance, senior managers expect evidence of how IT influences prospective measures such as the firm’s market value. We examine the efficacy of IT’s influence on firm value combined with measures of financial performance for non-publicly traded (NPT) hospitals that lack conventional market-based measures. We gathered actual sale transactions for NPT hospitals in the United States to derive the q ratio, a measure of market value. Our findings indicate that the influence of IT investment on the firm is more pronounced and statistically significant on firm value than exclusively on the accounting performance measures. Specifically, we find that the impact of IT investment is not significant on return on assets (ROA) and operating income for the same set of hospitals. This research note contributes to research and practice by demonstrating that the overall impact of IT is better understood when accounting measures are complemented with the firm’s market value. Such market valuation is also critical in merger and acquisition decisions, an activity that is likely to accelerate in the healthcare industry. Our findings provide hospitals, as well as other NPT firms, with insights into the impact of IT investment and a pragmatic approach to demonstrating IT’s contribution to firm value.

Examining the Impact of Information Technology on Healthcare Performance: A Theory of Swift and Even Flow (TSEF) Perspective

Sarv Devaraj et al. — Tue, 30 Apr 2013 14:46:26 PDT

The impact of information technologies on manufacturing operations and performance is well established. However, scant research has been devoted to examining information technology (IT) investment among hospitals and how it influences patient care and financial performance. Using the lens of the Theory of Swift Even Flow (TSEF), we present an operations management-based perspective on the effect of IT in streamlining hospital operations. Specifically, we examined the role of IT on patient flow and its consequences for improved hospital efficiency and performance. Analysis of data from 567 U.S. hospitals shows that IT is associated with swift and even patient flow, which in turn is associated with improved revenues. Interestingly, we find that the improvement in financial performance is not at the expense of quality because we find similar effects of IT and patient flow in improvements in the quality of patient care. Further, we observed differential effects of swift flow and even flow on various measures of hospital performance. Although swift flow affects financial performance, even flow primarily affects quality performance. Taken together, they have a mutually reinforcing overall impact on hospital performance. The implications of these findings for hospital decision makers are that patient flow is an important mediating variable that is affected by IT and can significantly affect the quality of patient care and financial performance.

Beyond the Numbers: Seven Stakeholders to Consider in Improving Acquisition Outcomes

David King et al. — Wed, 30 Jan 2013 11:32:06 PST

Dilettante, Venturesome, Tory and Crafts: Drivers of Performance Among Taxonomic Groups

Stanley W. Mandel et al. — Mon, 10 Dec 2012 07:27:55 PST

Empirical research has failed to cumulate into a coherent taxonomy of small firms. This may be because the method adapted from biology by Bill McKelvey has almost never been adopted. His approach calls for extensive variables and a focused sample of organizations, contrary to most empirical studies, which are specialized. Comparing general and special purpose approaches, we find some of the latter have more explanatory power than others and that general purpose taxonomies have the greatest explanatory power. Examining performance, we find the types do not display significantly different levels of performance but they display highly varied drivers of performance.

Crisis in American Information Systems Education: Innovations to Address the Threat of Offshoring

Kate Kaiser et al. — Thu, 15 Nov 2012 13:32:51 PST

Our classrooms are nearly empty. The job prospects for our graduates are bleak. Offshoring is reconfiguring the American information technology industry. What are we to do? After years of unprecedented growth, demand, and skill shortages, IT faculty find themselves in a new environment. As a shortterm solution, some faculty are scrambling to develop and redesign courses. This is not enough and will not sustain the fundamental shifts needed in a global economy. How can individual faculty, Information Systems departments, and schools respond to survive the rapidly changing landscape? The situation calls for innovations in academic delivery. Academics must serve as examples of agility to students by rethinking and revising curricula. We have responded when faced with other changes such as DBMS, end-user computing, networking, and e-commerce when it was in our favor. Can we do it again? How do we instill in students how they can become innovators and not merely problem solvers? What new organizational forms can academic programs take to help students bridge global teams? The panel will discuss innovations to address the crisis of low enrollments and dated skill sets. The format lists panelists in order of presentation along with their relevant expertise.

Managing the Psychological Contract for Improved Knowledge Sharing: The Role of Human Resource Management

Monica Adya — Fri, 09 Nov 2012 08:51:22 PST

Workforce Diversity in the IT Profession: Recognizing and Resolving the Shortage of Women and Minority Employees

Mike Gallivan et al. — Wed, 07 Nov 2012 12:43:27 PST

There is growing evidence of another impending skills shortage of IT professionals, with a significant contributor being the underrepresentation of women and minorities in the IT workforce (Office of Technology Policy, 1999; Freeman & Aspray, 1999). There are claims that if minorities and women were included in the IT workforce at rates close to their representation in the general population, this shortage could be ameliorated or resolved entirely. Moreover, achieving a greater representation of women and minority employees in the IT workforce may enhance creativity, performance and product markets (Panteli et al., 2001). Women and minorities are underrepresented in the IT fields, and as employers struggle to recruit an adequate number of IT skilled workers, large pools of potential talent remain untapped (ITAA, 2000). According to statistics from the U.S. Department of Commerce, women represent 46% of the total workforce, but under 30% of the IT workforce. African- and Hispanic-American employees hold only 5% of IT workforce jobs, yet comprise over 12% of the US population (NSF, 2000). There are two related problems – attracting women and minorities to IT-related

The Distributed User Modeling Shell System (DUMSS): A Conceptual Framework for Eliciting User Models

Thawatchai Piyawat et al. — Mon, 05 Nov 2012 12:19:38 PST

With the advances in communication technology, large volumes of information can transfer across continents within a fraction of a second. Nevertheless, computer users still suffer from unpleasant situations when they interact with systems and are required to adapt to systems rather than the other way round. User modeling aims to overcome this problem by enabling computer systems to interact with users according to the users’ models, i.e., goals, knowledge, and preferences of users. Although, user modeling has shown invaluable benefits, methods of capturing user information to build precise and useful user models are still in their early states. This paper proposes a new approach for gathering user information by pooling the information from different systems. This concept, entitled Distributed User Modeling (DUM) is based on a method in which sensors built into each system contribute specific user information to the pooling. Having multiple sources of user information increases the possibility that a system can generate reliable user models. A general model of DUM is presented in this paper. The conceptual framework of the Distributed Fuzzy Object-Oriented User Modeling System (DFOOUMS) that uses DUM as its basis structure is also presented.

Designing Effective Forecasting Decision Support Systems: Aligning Task Complexity and Technology Support

Monica Adya et al. — Fri, 02 Nov 2012 09:07:30 PDT

Evaluate Your Business School’s Writings As If Your Strategy Matters

John Cotton et al. — Wed, 10 Oct 2012 08:44:10 PDT

Business school publications are widely criticized for their lack of managerial or teaching relevance. One reason for this criticism is that business school scholarship is typically evaluated purely in terms of one type of work: academic journal articles that are meant to be read by other scholars. However, academics produce multiple types of publications, and business schools serve a wider range of stakeholders. These other stakeholders are often central to the schools’ purposes and may be critical in acquiring resources. These stakeholders probably prefer to see scholarship that is relevant for students or for practitioners. They may prefer scholarship that is ethically relevant or regionally relevant and otherwise different from the model that dominates U.S. journals. Technologies are now available to measure the impact of writings in a much wider range of venues than covered by the Social Sciences Citation Index in the Web of Science. Moreover, a wider range of measures, such as the size of writings’ readership, may be needed. We consider these issues and present some recommendations, arguing that faculty evaluations should follow an intentional strategy and not necessarily conform to the traditional default.

Making Sense of Entrepreneurship Journals: Journal Rankings and Strategy Choices

Alex Stewart et al. — Tue, 18 Sep 2012 08:11:57 PDT

Dozens of peer-reviewed, English language journals are currently published in our field. How ought we to evaluate them? This paper seeks to answer this question. To do so, we utilize both relevant literature and data on Entrepreneurship journals. The literature derives from both information science and other research areas that reflect on their journals. The data derives from six citation measures from Google Scholar, Scopus, and Web of Science. We find 59 currently published English language, peer reviewed journals in Entrepreneurship. Contestable judgments based on their impact measures suggest that one of these 59 could be considered as “A+, four as “A”, five as “AB”, eight as “B”, four as “BC”, 23 as “C”, thirteen as “barely detectable”, and one as “insufficient data but promising”.

Journal rankings affect the resources and prestige accorded to business schools, disciplines and subdisciplines, and individual scholars. However, the need to fit evaluations to school strategy implies that no rating system, ours included, is definitive. Multiple measures are needed, letter grades are misleading, and journal rankings should match the institution’s strategy and priorities in stakeholder service. A wider purpose of this study is to alert readers to the range of current methodologies and the limits of conventional rankings. Our conclusions appear innocuous, but standard practice is to use restrictive measures, to employ letter grades, and to prioritize only one stakeholder: scholars. These practices are poorly suited to the Entrepreneurship field.

Expert Systems Construction [Encyclopedia Entry]

Victoria Y. Yoon et al. — Wed, 15 Aug 2012 14:11:57 PDT

Foreword: Why Become a Business Anthropologist?

Alex Stewart — Tue, 10 Jul 2012 12:54:05 PDT

Considerations for CRM Implementation: Part 3

Mark Cotteleer et al. — Wed, 30 May 2012 12:07:31 PDT

In our two previous Business-IT Strategies E-Mail Advisors (9 and 23 January) we introduced discussions of our view that history matters when implementing customer relationship management (CRM) in the context of previous (or ongoing) enterprise resource planning (ERP) efforts. Topics have included the need to consider overlapping functionality, data standards, and data duplication. In this Advisor, we want to alter course a bit and address the training for, and the adoption of, CRM applications by end users. Here we think that managers sometimes rely too much on the experience they have gained through ERP and other back-office information technology implementation.

Considerations for CRM Implementation: Part 2

Mark Cotteleer et al. — Wed, 30 May 2012 12:03:52 PDT

In our prior Business-IT Strategies E-Mail Advisor (9 January 2002), we introduced a discussion of our view that history matters when implementing customer relationship management (CRM) in the context of previous (or ongoing) enterprise resource planning (ERP) efforts. At that time, we pointed out the existence of functionality overlaps between ERP and CRM applications and suggested a rule for deciding which functionality to apply. In this second Advisor, we want to suggest two other areas where prior effort and investment in the enterprise-level information technology architecture should be respected: data standards and data duplication.

Considerations for CRM Implementation: Part 1

Mark Cotteleer et al. — Wed, 30 May 2012 11:58:06 PDT

Customer relationship management (CRM) has emerged as the next major wave in the movement of firms toward the goal of tying together their value chain through the use of enterprise-level information technology. In fact, CRM seems to have picked up where enterprise resource planning (ERP) investment left off in the late 1990s, consuming tens of billions of dollars in investment and experiencing annual growth in the 30%-40% range. A question to be faced now is: how should firms be attempting to manage this next wave of enterprise-level information technology investment?

Packaged Software Applications: Road to Improvement or Competitive Parity?

Mark Cotteleer — Tue, 29 May 2012 13:32:51 PDT

I may never be as famous a golfer as Tiger Woods, but the people who make Titleist golf clubs suggest (through their commercials) that I can still play at his level if I use the same equipment as he. Those who have seen me swing a club doubt that this is the case. As with the game of golf, the assumption that two firms that invest in the same technology will experience similar business results leaves ground for skepticism.

Modifying Enterprise-Level Packages

Mark Cotteleer et al. — Wed, 23 May 2012 15:09:12 PDT

Managers often struggle with the decision of whether or not to significantly modify the customer relationship management (CRM) and other software packages they have just purchased. Often companies decide to make changes to the packaged application to meet their business requirements. This is very common and often times necessary, but should be undertaken with foresight and understanding of the consequences.

Improving and Leveraging Your Forecasting Practice

Monica Adya — Tue, 22 May 2012 13:51:23 PDT

This issue of Cutter Benchmark Review takes a departure from our typical interests. As you know if you closely follow CBR, we focus on technology issues with either a technical or managerial angle. A recent example of the first approach is our July 2007 issue on agile project development (Vol. 7, No. 7), while an example of the second approach is the February 2008 issue on making and using business cases (Vol. 8, No. 2). Given the nature of our interest in information systems and their management, we often produce an issue that is at the crossroads of the technical and managerial arenas. Our issues on the agile enterprise (April 2008, Vol. 8, No. 4), mashups (March 2008, Vol. 8, No. 3), and open innovation (December 2007, Vol. 7, No. 12) represent recent examples.

Data Mining in Health-Care: Issues and a Research Agenda

Monica Adya — Mon, 07 May 2012 12:56:34 PDT

While data mining has become a much-lauded tool in business and related fields, its role in the healthcare arena is still being explored. Currently, most applications of data mining in healthcare can be categorized into two areas: decision support for clinical practice, and policy planning/decision making. However, it is challenging to find empirical literature in this area since a substantial amount of existing work in data mining for health care is conceptual in nature. In this paper, we review the challenges that limit the progress made in this area and present considerations for the future of data mining in healthcare.