Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=vjeb20
Download by: [Universitas Maritim Raja Ali Haji] Date: 11 January 2016, At: 21:01
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Anatomy of a Scan: Digital Market Intelligence and
Economic Literacy in the MBA Curriculum
E. Vincent Carter
To cite this article: E. Vincent Carter (2013) Anatomy of a Scan: Digital Market Intelligence and Economic Literacy in the MBA Curriculum, Journal of Education for Business, 88:4, 194-201, DOI: 10.1080/08832323.2012.668392
To link to this article: http://dx.doi.org/10.1080/08832323.2012.668392
Published online: 20 Apr 2013.
Submit your article to this journal
Article views: 52
ISSN: 0883-2323 print / 1940-3356 online DOI: 10.1080/08832323.2012.668392
Anatomy of a Scan: Digital Market Intelligence
and Economic Literacy in the MBA Curriculum
E. Vincent Carter
California State University, Bakersfield, Bakersfield, California, USA
This pilot study examined an innovative rubric designed to overcome the deficiencies of present environmental scanning frameworks. The Anatomy of a Scan rubric resolves two problems associated with environmental scanning instruction. First, the need for simpler rubric designs with familiar formats arises because digital knowledge economy intelligence exceeds the ca-pabilities of existing scanning rubrics, given business education course delivery constraints. Second, the need for improved economic information literacy arises because knowledge econ-omy dynamics expand the breadth and depth of digital market intelligence. By using economic market intelligence to anchor environmental scanning, the anatomy rubric improves students’ strategic focus with conceptual advantages and raises economic literacy with empirical appli-cation.
Keywords: business education, cognitive rubric, digital intelligence, economic literacy, environmental scanning, knowledge economy, market intelligence, strategic planning
Business education is adapting to the societal transition to-ward a knowledge economy. The knowledge economy is more dynamic and uncertain than traditional markets (Eisen-hardt, 1989), due to the span and speed of digital intelligence. Environmental scanning reduces the uncertainty created by uncontrollable market dynamics with strategic market in-telligence (Albright, 2004; Choo, 2001; M. B. Wood, 2010). This bridging of external and internal planning factors makes environmental scanning a business education mainstay (Stoeffels, 1994).
As a pedagogical technique, environmental scanning re-quires information literacy to interpret strategic market in-telligence. In this study, economic information literacy is the ability to access, analyze, and apply facts about the knowl-edge economy environment. The Association to Advance Collegiate Schools of Business (AASCB) (2003) requires business education curricula to deliver both information tech-nology and information literacy skills to meet assurance of learning standards. These information literacy skills for scan-ning economic intelligence parallel the knowledge types in Bloom’s taxonomy of educational objectives (Bloom, 1956).
Correspondence should be addressed to E. Vincent Carter, California State University, Bakersfield, Department of Management & Market-ing, 9001 Stockdale Highway, Bakersfield, CA 93311, USA. E-mail: ecarter2@csub.edu
Cognitive rubrics are indispensable for developing the critical thinking skills required to perform environmental scanning. Cognitive rubrics operationalize theoretical con-cepts into practical competencies that can be learned and performed by business students. Typically, these cognitive rubrics are found in learning objectives and instructional ma-terials. This study examines the sufficiency of environmental scanning rubrics presently used by business educators, and advances a more suitable design for knowledge economy intelligence.
LITERATURE REVIEW: LEVERAGING ECONOMIC INFORMATION LITERACY
Complexity is rising in the knowledge economy because of expanded digital connections and dynamic interac-tion among environmental stakeholders. The external en-vironment now spans preknowledge economy boundaries such as social–strategic planning, macro–micro market, public–private sector, and financial–intellectual capital. Man-aging the network enterprise in complex and dynamic envi-ronments requires environmental scanning techniques that are strategically focused and cognitively framed. Simplicity in the design of scanning rubrics improves strategic focus by guiding managers toward market intelligence with high macro pattern relevance and high micro performance results.
ANATOMY OF A SCAN 195
As a result, business educators and executives improve envi-ronmental scanning skills.
Concurrent with rising complexity, the knowledge econ-omy environment is becoming digitally abstract. Hypertext programming codes digital content with instructions for ac-cessing deeper layers of data, documents, diagrams, and mul-timedia displays. Given these deep layers of digitally abstrac-tion, scanning the knowledge economy environment requires greater familiarity with the market intelligence format (scan-ning array) and management interpretation findings (strategic action). Designing scanning rubrics based on familiar mod-els can help business educators mitigate the lack of literacy regarding market intelligence content.
Information Literacy in Business Education
Information literacy is an academic and practical skill an-chored in library science. To be information literate, a person must be able to recognize when information is needed and has the ability to locate, evaluate, and use effectively the needed information. (American Library Association, 1989, p. 2)
Information literacy has gained greater attention with the proliferation of digital technology applications in so-ciety. Students are required to access, analyze, and apply electronic knowledge in a proficient manner (American As-sociation of School Librarians, 2007; Dunn, 2002; Eisen-berg, 2008). Ironically, information literacy is rarely ad-dressed in the business education literature (Goel & Straight, 2005; Hawkes, 1994; Heinrichs & Lim, 2009; Sterngold & Hurlbert, 1998). This information literacy neglect is con-trary to AACSB (2003) standards for capacities developed through knowledge and skills of a general master’s level pro-gram (Pringle & Michel, 2007).
Economic Literacy for Business Education
Economic literacy, as a specific type of information liter-acy, pertains to the awareness, access, assessment, and ap-plication of knowledge about the economy and its implica-tions. As the foundation for free market enterprise and the discipline specific academic programs, economic literacy is vital to business education. But, economic literacy is not compartmental. Improving economic literacy contributes to students’ overall business acumen. Economic literacy be-comes more strategically reliable and environmentally rep-resentative as the digital knowledge economy encompasses societal dimensions. Strategic intelligence is more reliable because economic data directly improves market predictions and management performance. Economic information is also more representative of environment trends, because knowl-edge economy market intelligence spans the boundaries of traditional environment factors. Demographic and sociocul-tural tendencies are derived from production and purchase data, while technology trends can be forecast from financial
investments in research and development. Bond ratings tell the markets odds on political policy and legal regulations are reflected in equity patterns. Even ecological factors can be discerned from commodity price fluctuations.
Compelling evidence exists for the merits of economic literacy in business education. Top academic journals and research centers advance the merits of economic education. Also, a credible set of economic indicators already exists (Conference Board, 2012). Recently, the Council for Eco-nomic Education (CEE) launched a national campaign for economic literacy and support for economic literacy reaches the upper echelon of academia and commerce. The CEE website and resources demonstrate the growing commit-ment to furthering economic literacy with interactive activ-ities and educational content tailored to young adult learn-ers (CEE, 2013). Projects have been initiated by leading universities, businesses, and regional Federal Reserve Bank presidents—including the national College Fed Challenge (Dodge, 2011; Federal Reserve Bank of Minneapolis, 1999, 2002; Federal Reserve Bank of Richmond, 2011; National Council for Economic Education, 2005). The common de-nominator among these economic literacy approaches is a view that the economy provides reliable and representative signals of the societal factors influencing business strategy. By designing strategic scanning rubrics that access digital market intelligence, students will be better prepared to ana-lyze these vital economic signals.
PROBLEM: THE BOUNDED RATIONALITY OF STRATEGIC SCANNING RUBRICS
Taken together, the knowledge economy environment issues of intelligence complexity and abstraction pose serious chal-lenges for business planning instruction and implementation. The cognitive and course delivery constraints of business ed-ucation render conventional environmental scanning meth-ods less suitable for information intensive digital markets. Appropriately designed cognitive rubrics can reduce knowl-edge economy complexity with simplified heuristics that fo-cus strategic scanning skills. Similarly, course delivery is aided by depicting cognitive scanning rubrics with familiar models that filter abstract market intelligence into intuitive economic literacy skills.
This problem of cognitive scanning rubrics designed with insufficient decision support capability for environmental intelligence conditions mirrors Simon’s (1991) theory of bounded rationality. That theory shows how management performance and market opportunity are constrained by a misfit between cognitive approaches and knowledge conditions—commonly described as analysis paralysis. Business strategists describe this environmental scanning dialectic between external market sensing and internal management strategy as sense and respond (Bradley & Nolan, 1998; Haeckel, 1999).
For business students, the wider span of accessible ex-ternal environment facts and the faster speed of digital ap-plications create bounded rationality between conventional scanning rubrics and the ample market intelligence. Less is more, when business instruction increases analytical clar-ity with anatomical economic categories. Simon (1991) described this type of analytical clarity as rational optimiza-tion that comes from realigning bounded raoptimiza-tionality. Busi-ness educators will benefit from an environmental scanning rubric that transforms passive situational descriptions into actionable strategic analysis.
Assessing Conventional Environmental Scanning Rubrics
A preliminary assessment of conventional environmental scanning rubrics helps to clarify the parameters required to address the knowledge economy environment. An optimal scanning rubric must mitigate the bounded rationality of busi-ness education arrangements to deliver actionable market in-telligence. Three cognitive rubrics are typically used to teach environmental scanning—situation analysis, product–market matrix, and strengths, weaknesses, opportunities, and threats (SWOT) analysis.
Situation analysis is a standard business strategy rubric for scanning the entire set of external environment factors. Modeled on the scientific method, strategic planning begins by examining the conditions that influence strategic planning problems. This first stage appraises the market situation by scanning six environment factors—economic, demographic, sociocultural, political-legal, technological, and ecological-natural (Kotler & Armstrong, 2011).
Another cognitive rubric for aligning the external mar-ket environment with internal management execution is the Ansoff (1957) product–market matrix for strategic planning. The matrix juxtaposes strategies for changing external mar-kets with strategies for changing internal products, based on the competitive environment situation. The resulting four strategy choices are market penetration (no change), prod-uct development (new prodprod-uct), market development (new market), and differentiation (new product and new market).
One of the most popular cognitive rubrics for environ-mental scanning is known as SWOT analysis. The goal of business planning is to find a sustainable fit between inter-nal strengths and weaknesses and exterinter-nal opportunities and threats. In practice, SWOT analysis is often used to compile an encyclopedic cache of market intelligence. Less empha-sis is placed on critically analyzing SWOT category intelli-gence to impart strategic problem solving skills (Novicevic, Harvey, Autry, & Bond, 2004).
Unfortunately, the academic and strategic utility of these three established rubrics is constrained by a knowledge econ-omy environment that exceeds the boundaries of their cog-nitive design. The wide scope of situation analysis reduces strategic focus, resulting in analysis paralysis. Thus, situa-tion analysis is too inclusive as a cognitive rubric to scan for
critical knowledge economy insights. By contrast, the nar-rowly aimed product/market matrix cannot capture knowl-edge economy insights that span environmental boundaries. So, it is too inhibiting for scanning the digital breadth and depth of the knowledge economy environment. Finally, the mosaic of SWOT analysis category associations generates excessive digital market intelligence and makes environ-mental scanning instruction too intricate for focused strategy decisions.
HYPOTHESES: DESIGNING AN ECONOMIC LITERACY SCANNING RUBRIC
A new strategic rubric named the Anatomy of a Scan is designed to address the inadequate framing of market intel-ligence access (breadth) and analysis (depth) identified for three conventional methods. Removing these limitations ren-ders a scanning rubric that achieves strategic focus by cogni-tively framing economic market intelligence. Consequently, a logical litmus test would be to evaluate whether the new rubric improves economic information literacy. While ex-ploratory, these empirical economic literacy findings can of-fer clues about the anatomy rubric’s suitability for scanning the digital market intelligence. Accordingly, the empirical data hypothesis is that the Anatomy of a Scan rubric im-proves economic literacy skills. A related conceptual design hypothesis is that the Anatomy of a Scan rubric is a simpler and more intuitively familiar instructional tool for accessing and analyzing digital market intelligence.
Anatomy Rubric Scanning Parameters
The Anatomy of a Scan design can be described using the parameters used to assess conventional rubrics (see Table 1). The anatomy model resolves three deficiencies of conven-tional scanning rubrics by increasing the simplicity and fa-miliarity of scanning intelligence for business course instruc-tion. It is not too macro, too micro, or too mosaic.
A need for simplicity arises because digital knowledge economy networks span the external environment factors, creating a breadth of market intelligence that reduces the efficacy of conventional rubrics. Simplicity is addressed at the macro level by reducing the span of external environ-ment factors to 10 straightforward economic indicators. Still, economic trends reliably reflect macroenvironment patterns. Simplicity is also built into micro level execution decisions by focusing on reliable economic market intelligence that leads to actionable strategic results. The anatomy rubric helps stu-dents analyze the connections between economic mapping and strategic maneuvering on the business planning chess-board.
The requirement of familiarity stems from the ability to probe digital content links and access deeper layers of mar-ket intelligence abstraction. Although these digital content properties can be used to enhance strategic scanning acumen,
ANATOMY OF A SCAN 197
TABLE 1
Anatomy of a Scan Environmental Scanning Rubric
The market anatomy: Body of the economy
Leading economic indicators basket (market organs and functions) Investment (head) Supply (side) Demand (side) Savings (feet)
1. The U.S. Department of Labor’s monthly report on the unemployment rate, average hourly earnings and the average workweek hours from the Employment Situation report (employment report).
x (Income)
2. The U.S. Department of Labor’s weekly report on first-time claims for state unemployment insurance. Initial jobless claims.
x (Income)
3a. The Census Bureau’s monthly consumer goods and materials report from the Preliminary Report on Manufacturers’ Shipments, Inventories & Orders (from the factory orders report).
x (B2C) x (B2C)
3b. The Quarterly Services Survey,
http://www.census.gov/services/index.html.
4. The Institute for Supply Management’s monthly ISM Index of Manufacturing including: supplier deliveries, imports, production, inventories, new orders, new export orders, order backlogs, prices and employment (Purchasing Managers’ Index).
x (B2B) x (B2B)
5. The Census Bureau’s monthly nondefense capital goods report from the Preliminary Report on Manufacturers’ Shipments, Inventories, and Orders (from the factory orders report).
x (B2B) x (B2B)
6. The Census Bureau’s monthly report on building permits from the Housing Starts and Building Permits report (from the housing starts report).
x (Property/land) x (B2B)
7. The S&P 500 as a good measure of stock equity price accounting for 500 largest companies in the U.S.
x (Equity)
∗∗Supplemental∗∗
Commodities Precious metals Currency 8. The Federal Reserve’s inflation-adjusted measure of the M2 money
supply.
x (Time deposits). x
9. The difference (spread) between interest rates of 10-year Treasury notes and federal funds rate.
x (Debt/bonds)
10. The University of Michigan Consumer Sentiment Index’s consumer expectations.
x (psychol.)
business students may require a familiar rubric format for in-terpreting market intelligence findings. The anatomy rubric is designed with a familiar format based on an intuitive un-derstanding of the human body.
Assigning anatomy body parts to forces in the economy is central to the rubric’s design. The four category anatomy reduces the rubric’s bounded rationality by making it simpler to accessed and analyze market intelligence. Market intelli-gence from ten economic indicators is filtered into four key market drivers of strategic decisions. This familiar format should also be more suitable for sifting through abundant real-time digital market intelligence. Still, familiarity must be backed up with fact for the anatomy rubric to gain cre-dence among business educators. For that reason, explaining the human body analogy is crucial.
To begin, the purpose of the economy is to allocate so-cietal resources and raise individual prosperity. Investment, in the form of capital or another asset, is required to pro-duce, transport, or transform any resource, as well as to pay individual salaries and financial returns. In that sense,
invest-ment is the head of the economy because it starts resource movement and steers market direction. All markets reflect this capitalist principle of an investment led economy.
These investment funds differ from savings, the feet of the economy. Savings is money, or another asset, put into reserve and not allocated for financial return. Examples include bank savings accounts, money market funds, and balance sheet cash. A familiar quip is investment puts money to work and savings does not. Similar to feet, savings support the body by keeping pace while the head is pointing. For the economy, savings is a foundation and safety net for market investments. Without savings, economic activity is financed by debt as a bond investment. When the head falls, flat-footed savings is the only thing that holds the economy upright. So, savings is capital retained for reliance, which keeps the economy grounded, whereas investment is capital risked for return which keeps the economy growing.
The sides of the anatomy follow the head’s direction to propel the economy. The side to side motion propelling the body is analogous to supply and demand exchanges moving
the economy. Continuous supply and demand transactions funnel resource flows to expand the economy in the direc-tion of investment, like a spiraling twister ambling forward, backward, up and down. Economic indicators capture the supply–demand axis of human labor–income, around which the economy spins. In addition, economic production outputs and economic psychology inputs signal the respective health of companies and consumers. Economic growth from supply and demand either flows to the feet as savings or flows to the head for reinvestment.
Each body part performs the dual function of environ-ment sensing and execution responding. Unlike economic indicators, the categories of investment, supply, demand, and savings are instinctive for business planning. As the head, investment indicators provide macro vision of future mar-ket directions, as well as insight regarding the micro value of present market performance. The supply side indicators signal macro-output trends across industries and monitor the micro supply chain resource flows. Demand side indicators mirror supply side motions by charting macro shifts in ag-gregate customer employment, income, and perception. In addition, demand side indicators shed light on the micro spending and sentiment of individual customer segments. As the feet, savings indicators survey the macro base of account deposits and size up the micro share of capital that is not actively deployed in the market. These dual macro–micro properties improve the anatomy rubric’s utility across the business education curriculum.
Using analogous human body forms and affiliated macro/micro functions, the anatomy rubric analyzes eco-nomic market intelligence to support strategic management decisions. Of course, the field of econometrics (Davidson & MacKinnon, 2004) is replete with analytical models that guide the decisions of policy makers and managers. Al-though far less robust than econometric models, the anatomy rubric achieves Bloom’s (1956) pedagogical objective of analysis using the types of prized data analysis skills touted by top business curricula (Korn & Tibken, 2011). It avails an intuitive interface between societal patterns and strategic planning.
Anatomy Rubric Scanning Procedure
The anatomy rubric is distilled into a simple heuristic to guide students’ analysis of the economic environment using the standard basket of leading indicators:
S: Separate indicators into four anatomy categories to iden-tify their economic role (investment=head, supply=
side, demand=side, savings=feet).
C: Compare the increase/decrease in each indicator with pre-vious period(s) and chart the resulting trend patterns for each market anatomy category.
A: Analyze relationships among the four market anatomy categories and assess the influences of individual eco-nomic indicators on the directional patterns.
N: Navigate strategic marketing decisions by incorporating economic intelligence from the market scan anatomy analysis patterns.
METHODOLOGY: FRAMING ECONOMIC LITERACY SCANNING INSTRUCTION
A pilot study of the Anatomy of a Scan rubric and procedure was conducted in a master of business administration (MBA) marketing strategy course at a regional university. The ab-sence of prior iterations of this MBA course module makes this a purely exploratory pilot study. Likewise, the absence of environmental scanning studies for assessing economic literacy in the business education literature makes standard research benchmarking and statistical baseline comparisons less feasible. However, these considerations are common for pilot studies of innovative instructional modules, when for-mal research arrangements with large samples and a control group structure cannot be done. Instead, based on cursory class observations, a small convenience sample is queried re-garding the conceptual advantages of the Anatomy of a Scan design. In addition, data collected from an informal experi-ment with Anatomy of a Scan procedures offers a prelimi-nary empirical analysis of economic literacy improvements among the MBA students.
Administering the Anatomy of a Scan Module
The three-week Anatomy of a Scan module was administered to 24 MBA students as an individual assignment preceding a group marketing strategy project. The module followed basic marketing strategy discussions in prior class sessions, including breakout group activity for brief hands-on appli-cation of conventional scanning rubrics. The module started with a topic discussion to establish an economic literacy base-line. After generating several comments about the economy as an external environment factor, students were queried on economic terminology. Next, students were briefed on the Anatomy of a Scan rubric and asked to perform a series of hands-on real-time economic environment scans.
FINDINGS: PILOT STUDY OF ANATOMY RUBRIC AND ECONOMIC LITERACY
This pilot study of the Anatomy of a Scan rubric is aimed at two research objectives:
Conceptual advantages: Improve awareness of economic literacy facts:
a) economic indicators
• types (leading, coincident, lagging),
• template (10 leading indicators),
ANATOMY OF A SCAN 199
b) economy categories (investment, supply, demand, savings), and
c) external environmental factors encoded in eco-nomic indicators (ecoeco-nomic, demographic, socio-cultural, technological, political, ecological).
Empirical application: Improve performance of economic literacy functions:
d) macro environment scanning, and e) micro execution strategy.
Conceptual Advantages of the Anatomy Rubric
The MBA class observations and informal student responses confirmed the hypothesized conceptual advantages, regard-ing the anatomy rubric’s design simplicity and familiarity. Compared to conventional scanning methods, the anatomy rubric proved to be more suitably framed to distill the complex breadth of knowledge economy information into focused strategic intelligence. Students were more adept at accessing and analyzing market intelligence using the anatomy rubric. Economic indicators were better understood and examined to for insight regarding external environment factors. Using the hyperlink features of digital market intelligence, students drew valid conclusions about emerging demographic, sociocultural, and technology trends from the economic indicators scanned. Although economic intelligence is also capable of revealing political-legal and ecological trends, insights about those two environment factors were not reported by students.
Breakout group observations of students’ scanning ac-tivity verified the hypothesized deficiencies associated with each conventional scanning rubric. Situation analysis was used for compiling exhaustive data that prevented time and attention from being focused on strategic implications. Nar-rowly calibrated product–market matrix information inhib-ited students’ knowledge of external environment trends. The SWOT analysis was used to compose intricate associa-tions across all four categories that complicated the students’ strategic focus.
In terms of imparting familiarity of scanned intelligence content, the anatomy rubric was conceptually more benefi-cial that conventional frameworks. These benefits are largely a result of the anatomy rubric’s use of leading economic indicators to reduce market intelligence breadth, as well as familiar human anatomy categories to reinforce the purpose of each indicator.
Empirical Application of the Anatomy Rubric
The anatomy rubric also enhanced the application of scan-ning techniques. When using the rubric, MBA students gleaned strategic insights from scanned data with greater accuracy, precision, and timeliness. Scans performed using the anatomy rubric also strengthened the connection between
macro and micro market intelligence. For instance, equity in-vestment data was used to gauge macro economy directions, and also probed to guide micro execution decisions within targeted industries and markets. In a similar manner, demand indicators such as employment and consumer sentiment show collective demographic and sociological factors, as well as shape strategic choices for individual customer segments.
Empirical analysis of pilot study data shows improved economic literacy. Student responses during class discus-sions show low economic literacy prior to the Anatomy of a Scan module and high response accuracy afterwards (see Table 2). Pre–anatomy module findings record that although a large majority of students regarded economic intelligence as most pertinent to strategic planning and leading indica-tors to be the most relevant economy trends, only a third of students knew the three categories of economic indicators (leading, coincident, and lagging) and none could name more than three leading economic indicators. Post–anatomy mod-ule findings show that approximately 80% of the students had perfect recall of those two economic literacy questions, and the other 20% missing one or two of 10 indicators. These anatomy rubric learning improvements extend to students’ identification and interpretation of the ten leading economic indicators. The preanatomy mean of less than two of 10 was increased to a nearly perfect postanatomy mean of 9.7. Students were also able to explain the relationship between specific indicators and the standard set external environment factors.
Most essential, the empirical pilot study findings suggest that the Anatomy of a Scan rubric strengthened students’ awareness of macro economic market intelligence trends us-ing leadus-ing indicators, and structured their analysis of mi-cro strategy decision implications using the four-category anatomy format. Analysis of micro strategy decisions is op-erationalized by correct classification of data from leading economic indicators into the four anatomy categories, in-cluding the explanation of strategic implications. These im-provements are clearly shown in the pre- and postanatomy descriptive statistics for those strategic anatomy categories variables (C and D). Preanatomy results show that only one student could accurately classify economic indicators into the four anatomy format categories, and identify a strategic insight based on data in each category. Yet, for postanatomy findings, 21 of 24 students (88%) accurately accounted for all four categories, and three students correctly addressed three categories.
In the absence of a standard baseline for the statistical find-ings from this Anatomy of a Scan pilot study, the economics literature can help to validate the preanatomy economic lit-eracy levels reported for MBA students. Economics edu-cation scholars have established a generally accepted base-line for economic literacy among U.S. college students and adults (Albritton, 2006; Walstad & Allgood, 1999; Walstad & Rebeck, 2002; W. C. Wood & Doyle, 2002). Annual sur-veys of college students and college bound high school
TABLE 2
Descriptive Statistics for Macro–Micro Anatomy of a Scan Literacy
(A) Preanatomy: (B) Postanatomy: (C) Preanatomy: (D) Postanatomy:
Statistic # recall lead econ ind. # recall lead econ ind. # strategic categories # strategic categories
M 1.290 9.710 1.250 3.875
Mode 1 10 1 2
Minimum 0 8 1 3
Maximum 3 10 3 4
SD 1.042 0.624 0.532 0.338
n 24 24 24 24
Note.(A & B) Number (#) of 10leading economic indicatorsaccurately identified and explained. (C & D) Strategic categorization ofleading economic indicatorsintoanatomy categories: Number (#) of 4 accurate categories: investment, supply, demand, savings.
students consistently show that low economic literacy is not an anomaly (CEE, 2011a, 2011b). Comparable data from those surveys affirm this study’s preanatomy base-line statistics for questions on the economy, economic in-dicators, and applying economic intelligence. Consequently, the postanatomy economic literacy improvements from the Anatomy of a Scan module should be viewed by business educators as a plausible course outcome.
CONCLUSION: ECONOMIC ENVIRONMENT SCANNING LESSONS
Business education is only as good as the tools deployed to improve learning. This pilot study examines an innova-tive instructional tool designed to overcome the deficiencies of present environmental scanning frameworks, called the Anatomy of a Scan rubric. The anatomy rubric was found to resolve two interrelated problems associated with environ-mental scanning instruction in digital knowledge economy conditions.
First, the need for simpler rubric design with familiar for-mat categories arises because knowledge economy environ-ments generate an abundance of digital market intelligence that exceeds the capability of existing scanning rubrics, given business education course delivery constraints. Second, the need for improved economic information literacy arises be-cause knowledge economy dynamics expand the breadth and depth of digital market intelligence.
Digital economic data encompass broad latitudes of ex-ternal environment intelligence and encodes deep layers of strategic insight. So, by aiming environmental scanning in-struction at economic market intelligence, the Anatomy of a Scan rubric improves strategic focus and economic literacy. These hypothesized dual benefits are supported by informal observations and empirical findings from a convenience sam-ple survey of MBA students.
Research Limitations
Notwithstanding the merits reported for the Anatomy of a Scan rubric, this pilot study offers only initial exploratory
findings. Clear limitations exist regarding the anatomy rubric’s premise and proof. The premise of emphasizing economic data to perform strategic environmental scanning techniques can be challenged as being overly myopic. Re-ducing macro societal trends to tracking the economy may compromise other important scanning insights. For instance, appreciation for sociocultural diversity, political-legal ethics, and sustainable ecology might be glossed over without care-ful application of the anatomy rubric by business educators. Likewise, this study has not provided conclusive proof of the anatomy rubric’s amenable design for digital knowl-edge economy conditions, or its ability to improve economic literacy. Empirical findings lack the formal structure, large sample size, control group arrangement, and robust statisti-cal analysis to establish the anatomy rubric’s reliability as a universally valid business education tool. To that end, the Anatomy of a Scan rubric is advanced as a timely and tar-geted business education innovation for teaching environ-mental scanning skills.
REFERENCES
Albright, K. S. (2004). Environmental scanning: Radar for success. Infor-mation Management Journal,38(3), 38–45.
Albritton, F. P. Jr. (2006). Performance on tests of economic literacy: A com-parison of face to face with online instruction.First Monday,11. Retrieved from http://firstmonday.org/issues/issue11 10/albritton/index.html American Association of School Librarians. (2007). Standards for the
21st century learner. Retrieved from http://www.ala.org/ala/mgrps/ divs/aasl/guidelinesandstandards/learningstandards/standards.cfm American Library Association. (1989).Presidential committee on
informa-tion literacy, final report. Chicago, IL: Author.
Ansoff, H. I. (1957). Strategies for diversification.Harvard Business Review, 25, 113–124.
Association to Advance Collegiate Schools of Business (AACSB). (2003). Eligibility procedures and accreditation standards for business accredi-tation. Tampa, FL: Author.
Bradley, S. P., & Nolan, R. L. (1998).Sense and respond: Capturing value in the networked era. Boston, MA: Harvard Business School Press. Bloom, B. S. (1956).Taxonomy of educational objectives, handbook 1: The
cognitive domain. New York, NY: David McKay.
Choo, C. W. (2001).Information management for the intelligent organi-zation: The art of scanning the environment(3rd ed.). Medford, NJ: Information Today.
ANATOMY OF A SCAN 201
Conference Board. (2012). Economic indicators. Retrieved from http://www.conference-board.org/topics/subtopics.cfm?topicid=20& subtopicid=200
Council for Economic Education (CEE). (2011a).Test of understanding in college economics (TUCE). New York, NY: Author.
Council for Economic Education (CEE). (2011b).Campaign for economic literacy. Retrieved from http://www.councilforeconed.org/cel/ Council for Economic Education (CEE). (2013). CEE: Leading
or-ganization for economics and financial education. Retrieved from http://www.councilforeconed.org
Davidson, R., & MacKinnon, J. G. (2004).Econometric theory and methods. New York, NY: Oxford University Press.
Dodge, B. (2011).Economic forecasting: An internet webquest. Retrieved from http://webquest.org/index.php
Dunn, K. (2002). Assessing information literacy skills in the California State University: A progress report.The Journal of Academic Librarianship, 28, 26–35.
Eisenberg, B. M. (2008). Information literacy: Essential skills for the information age.Journal of Library & Information Technology, 28, 39–47.
Eisenhardt, K. M. (1989). Making fast strategic decisions in high velocity environments.Academy of Management Journal,32, 543–576. Federal Reserve Bank of Minneapolis. (1999). The economic literacy
project: Seeking a blueprint for America: Conference report. Minneapo-lis, MN: Author.
Federal Reserve Bank of Minneapolis. (2002). Economic and financial literacy moves from the schoolhouse to the statehouse: Participants at a Washington, D.C. conference suggest ways to further integrate eco-nomic education in school curriculums. Retrieved from http://www. minneapolisfed.org/publications papers/pub display.cfm?id=3404 Federal Reserve Bank of Richmond. (2011).College fed challenge
guide-book. Richmond, VA: Author.
Goel, R., & Straight, R. L. (2005). Usingr datasets for interdisciplinary business/economics projects. Journal of Education for Business,80, 59–68.
Haeckel, S. H. (1999).Adaptive enterprise: Creating and leading sense and respond organizations.Boston, MA: Harvard Business School Press. Hawkes, D. K. (1994). Information literacy and the business schools.Journal
of Education for Business,70, 54–61.
Heinrichs, J. H., & Lim, J.-S. (2009). Information literacy and office tool competencies: A benchmark study.Journal of Education for Business, 85, 153–164.
Korn, M., & Tibken, S. (2011, August 4). Schools plan leap into data.The Wall Street Journal, B12.
Kotler, P., & Armstrong, G. (2011).Principles of marketing(12th ed.). Upper Saddle River, NJ: Pearson–Prentice Hall.
National Council for Economic Education. (2005).Campaign for economic literacy: Test your own economic literacy. New York, NY: Author. Novicevic, M. M., Harvey, M., Autry, C. W., & Bond, E. U., III (2004). Dual
perspective SWOT: A synthesis of marketing intelligence and planning. Marketing Intelligence & Planning,22, 84–94.
Pringle, C., & Michel, M. (2007). Assessment practices in AACSB-accredited business schools. Journal of Education for Business, 82, 202–211.
Simon, H. (1991). Bounded rationality and organizational learning. Orga-nization Science,2, 125–134.
Sterngold, A. H., & Hurlbert, J. M. (1998). Information literacy and the marketing curriculum: A multidimensional definition and practical appli-cation.Journal of Marketing Education,20, 244–249.
Stoeffels, J. D. (1994).Strategic issues management: A comprehensive guide to environmental scanning.Tarrytown, NY: Elsevier Science.
Walstad, W. B., & Allgood, S. (1999). What do college seniors know about economics?American Economic Review,89, 350–354.
Walstad, W. B., & Rebeck, K. (2002). Assessing the economic knowledge and economic opinions of adults.Quarterly Review of Economics and Finance,42, 921–935.
Wood, M. B. (2010).Marketing plan handbook. Englewood Cliffs, NJ: Pearson–Prentice Hall.
Wood, W. C., & Doyle, J. M. (2002). Economic literacy among corporate employees.Journal of Economic Education,33, 195–205.
