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A hypothesis was drawn: H0 Having experience with podcasts has no effect on students' acceptance of replacing required textbooks with instructor selected learning outcome focused podcasts. Ha. Having experience with podcasts has a positive effect on students' acceptance of replacing required textbooks with instructor selected learning outcome focused podcasts. This study shows students’ experience with different information delivery formats has informed their preferences. Podcasting is deemed useful by students when the information meets student needs. Where faculty do not perceive one area of focus for the use of podcasts, students emphatically point to podcasts that include explaining how to solve specific problems and provide specific information. Students learn best by having information provided in chunks, which allow skimming for knowledge. This is analogous to skimming a text for answers. Student also noted that chunking improved understanding and retention. Students acknowledged in the survey that they are reading in a searching pattern in order to answer specific questions, rather than reading the entire chapter from beginning to end. The research does not show that textbooks should be thrown out altogether, but it does show that faculty should start including podcasts, whether self-produced or gathered, that are learning objective specific in their lesson plans. The survey indicates that many podcasts could improve use by tailoring podcasts to students use preference which indicates a focus on answering questions. This may lead to preference for chunking and indexed podcasts that are easily searchable. By doing this the faculty will be able to target specific learning outcomes and gain the attention of students, in turn increasing student learning and retention.


Anzai, Y. (2007). Empowering English Learning Utilizing Podcasts. In T. Bastiaens & S. Carliner, Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 265 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 Higher Education 2007 (pp. 10-15). Chesapeake, VA: Association for the Advancement of Computing in Education (AACE). Retrieved November 8, 2015 from http://www.editlib.org/p/26289.

Berger, E. (2007). Podcasting in Engineering Education: A Preliminary Study of Content, Student Attitudes, and Impact. Innovate: Journal of Online Education, 4(1), Brown, A., & Green, T. D. (2007). Video Podcasting in Perspective: The History, Technology, Aesthetics, and Instructional Uses of a New Medium. Journal of Educational Technology Systems, 36(1), 3-17.

Carnegie Mellon. (2007). A teaching with technology white paper: Podcasting [White paper] Retrieved from http://www.cmu.edu/teaching/resources/PublicationsArchives/StudiesWhitepapers/Podcasting_Jun07.pdf Cassidy, E. D., Britsch, J., Griffin, G., Manolovitz, T., Shen, L., & Turney, L. (2011). Higher Education and Emerging Technologies: Student Usage, Preferences, and Lessons for Library Services. Reference & User Services Quarterly, 50(4), 380-391.

Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology Review, 3, 149–210 Crofts, S., Diley, K., Fox, M., Retsema, A., & Williams, B. (2005). Podcasting: A new technology in search of viable business models. First Monday. 10(9), Retrieved October 21, 2015.

http://journals.uic.edu/ojs/index.php/fm/article/view/1273/1193 Donnelly, K. M., & Berge, Z. L. (2006). Podcasting: Co-opting MP3 players for education and training purposes. Online Journal of Distance Learning Administration, IX (III). Retrieved October 27, 2015, from http://www.westga.edu/~distance/ojdla/fall2006/donnelly93.htm Evans, C. (2008). The effectiveness of m-learning in the form of podcast revision lectures in higher education. Computers & Education, 50(2), 491-498.

Fizz, R. (2013). Podcasting 101: A guide to getting started. MIT News. Retrieved October 20, 2015, http://news.mit.edu/2013/podcasting-101-a-guide-to-getting-started Garver, M., & Roberts, B. (2013). Flipping & clicking your way to high-order learning. Marketing Education Review, 33(1), 17-21.

Government Accountability Office. (2013). College Textbooks: Students Have Greater Access to Textbook Information. GAO-05-806. Washington, DC: United States Government Accountability Office.

Khechine, H., Lakhal, S., & Pascot, D. (2013). University Students' Perception of the Pedagogical Use of Podcasts: A Case Study of an Online Information System Course. Journal of Education and Training Studies, 1(2), 136-151. EBSCOHost.

Mayer, R. E. (1997). Multimedia learning: Are we asking the right questions? Educational Psychologist, 32, 1-19.

GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 266 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 Mayer, R. E. (2003). The promise of multimedia learning: using the same instructional design methods across different media. Learning and Instruction. Elsevier Science. 13. 125-139.

McCoog, I. J. (2007). Integrated Instruction: Multiple Intelligences and Technology. Clearing House: A Journal of Educational Strategies, Issues and Ideas, 81(1), 25-28.

Parson, V., Reddy, P., Wood, J., & Senior, C. (2009). Educating an "iPod" Generation: Undergraduate Attitudes, Experiences and Understanding of Vodcast and Podcast Use. Learning, Media and Technology, 34(3), 215-228. Eric.

Robson, N. and Greensmith, J. (2010). Educational podcasts: Some early evidence and thoughts.

International Journal of Management Education 8(3), 107–17.

Silver, L. S., Stevens, R. E., & Clow, K. E. (2012). Marketing Professors’ Perspectives on the Cost of College Textbooks: A Pilot Study. Journal of Education for Business, 87(1), 1-6.

Webster, T. (2014). The Infinite Dial 2014 - Edison Research (Edison Research) Retrieved September 23, 2015, from: http://www.edisonresearch.com/the-infinite-dial-2014/ Webster, T. (2015). New podcasting statistics – Is the glass half-full, or half-empty? Edison Media

Research. Retrieved October 13, 2015, from:

http://www.edisonresearch.com/2007_podcast_statistics_analysis/ Zeng, L. (2009). Perceived values of podcasting: Predicting the adoption of podcasting among nonadopters. Business Research Yearbook: Global Business Perspectives, XVI (1), 101-106.


Dr. Nicole Ortloff-Wensel is an Assistant Professor at Our Lady of Holy Cross College. She has 15 years of experience in Business education related to management and economics and 18 years of practical experience in the private sector. She may be reached at nortloff@gmail.com.

GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 267 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1

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This study proposes a new procedure for using a combination of data envelopment analysis (DEA) and financial measures for corporate value assessment. DEA is a holistic method for evaluating corporate operational efficiency based on inputs and outputs of the firm’s production activities. Financial measures, on the other hand, evaluate corporate financial outcomes, such as return on assets (ROA) and return on equity (ROE), which are calculated from information in the firm’s financial statements. DEA operational efficiency, ROA, and ROE are linked in that they all assess the firm’s activity, but they are not necessarily consistent with one other. By definition, DEA emphasizes production activities, while ROA and ROE emphasize financial performance. In order to assess how each of these important aspects affects corporate values, the regression analyses are applied in this study.

JEL: L94, M21 KEYWORDS: Data Envelopment Analysis (DEA), Corporate Value, Operational Efficiency, Financial Performance, Electricity


The value of a corporation is often measured by the firm’s market capitalization, which is stock price multiplied by the number of shares outstanding. According to modern finance theory, capital markets are informationally efficient and stock prices fully and correctly reflect the available information to the values of stocks, which thus exhibit perfect foresight regarding the firm’s future cash flows. However, many finance studies have suggested that changes in stock prices are too volatile to be accounted for solely by changes in information about economic fundamentals. For example, concerns about the efficient market hypothesis were argued by Shiller (1981) and LeRoy and Porter (1981). A central objective of this study is to measure the discrepancy between actual and estimated corporate values, in which the estimated corporate values are based on the firms’ operational efficiency and financial performance. Further, this study discusses the factors that influence the discrepancy between actual and estimated corporate values. Based on the results of these analyses, a new procedure for examining the relationship between corporate value and financial performance and operational performance, estimated by use of data envelopment analysis (DEA), is proposed in this study. The remainder of this paper is organized as follows: The next section is a review of previous, relevant DEA studies. The following section explains the basic idea behind the proposed method, although empirical study is left for future work. The final section of this paper consists of a summary of this study and remarks regarding the plan for future research, which will include empirical analysis of electric utility companies.


Sueyoshi et al. (2009) examined a synergy effect between electricity services and gas services in the U.S.

electric utility industry. They compared electricity-specialized firms with diversified utility firms, based on their financial performance (measured by the Altman Z-score) and corporate value (measured by Tobin’s q ratio). Then, they confirmed the existence of the synergy effect in the two (electricity and gas) services GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 268 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 because the diversified firms outperformed the electricity specialized firms. Unfortunately, in their study they did not empirically measure the firms’ operational performance. In addition, they did not measure the causal relationships among operational performance, financial performance, and corporate value. As an extension of Sueyoshi et al. (2009), Sueyoshi and Goto (2011) fully utilized DEA in their investigation of the operational performance of 104 U.S. electric utility firms during 1990–2004. The Sueyoshi and Goto (2011) study, by analyzing causal relationships among the various performance measures, provided evidence of an operational synergy effect between electricity and gas in the U.S. electric utility industry.

As a natural extension of those two studies, this study examines the three types of measures: operational performance, financial performance, and corporate value, and applies them to regression analyses to estimate corporate value in an integrated framework. Sueyoshi and Goto (2011) used the three types of measures in a step-by-step manner, from DEA operational efficiency to corporate value, in order to classify samples into groups. In contrast, in this study regression analysis of the three types of measures is used in order to examine mutual relationships among them.


This study proposes the use of a combination of DEA and financial measures in order to assess corporate value, as depicted in Figure 1.

Figure 1: Structure of this Study’s Analysis vs. Sueyoshi and Goto’s (2011) Analysis

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This study examines the relationship among corporate value, financial performance, and operational efficiency As shown in Figure 1, Sueyoshi and Goto (2011) first used DEA to calculate operational efficiency and then classified the sample data by that efficiency. Second, they calculated measures of financial performance and then classified the sample data by those measures. Third, they calculated corporate value and then classified the sample data by those corporate values. Finally, they used a non-parametric statistical method in order to examine mutual relationships among the three performance measures. This study also calculates each performance measure, but the final objective is to estimate corporate value as a dependent variable based on the other two performance measures as independent variables, as described in equation (1) below.

= (, , ) + (1) in which V is corporate value as measured by Tobin’s q ratio, OE is operational efficiency as measured by a DEA model, FP is financial performance, measured by return on assets (ROA), return on equity (ROE),

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and other financial measures, and Z consists of variables that represent firms’ various other activities, such as environmental protection. The subscripts i indicates a firm (i=1,…,I) and t indicates a period (t=1,…,T).

e is an error term. Operational efficiency as measured by DEA represents the firm’s productive efficiency that is associated with a firm’s production technology. Financial performance measures represent the firm’s activity from the perspective of financial outcomes. These two types of measures are important factors in explaining corporate value. Z represents various non-financial factors that influence corporate value, such as the firm’s efforts in environmental protection and corporate social responsibility and overall economic or market conditions.


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