DB Forum 3: Chapters Three through Five
Dominic Bagley, Marieli Colon Padilla, James Engstrand,
Charleeta Latham, Kemakolam Ohale, Deidre Wilson
Liberty University
BUSI 600
Dr. Shawna Wentlandt
September 6, 2014
Forum 3
Management Setting Concepts, Constructs and Operational Definitions (3.3) Cooper and Schindler (2014), articulate that research requires the exploration of various questions in order to organize, understand, explain and forecast phenomena. These research inquiries must be guided through agreed upon definitions of the questions used to build the research product. These questions contain or use concepts, constructs and definitions (Cooper et al, 2014, p.50). A concept provides a common method for communicating information; it is a group of meanings and characteristics that impact events, objects, conditions, situations and behaviors (Cooper et al, 2014). Constructs are often abstract concepts; in fact concepts and constructs are easily mistaken. A construct is created by pairing less complex, more hardened concepts which provide a defined image or abstract idea enabling the researcher to organize the theory building purpose (Cooper et al, 2014). In research, definitions of concepts must be clearly defined by all parties involved in conducting the research. This will safeguard the integrity of the research. Without this synchronized understanding of definitions the output of the research will be damaged. Definitions in research consist of dictionary and operational. Dictionary definitions include a concept defined with a synonym. The operational definition contains specific criteria measuring and testing which refer to empirical data (Cooper et al, 2014p). Both definitional and operational definitions’ purpose is to afford an understanding and measurement of concept within the research body. The following terms would be examples of concepts: First-line supervisor; Overdue account; Line Management. These terms are simpler and more concrete and would be used in making up the more abstract constructs. Some examples of terms that fit as constructs would be Employee moral; Assembly line; Leadership; Union Democracy and Ethical standards. These terms are much more difficult to visualize and would be a good example of abstract ideas or images that lend themselves to the development of research theory building. When assigning the above terms with operational definitions we must ensure they are stated with specific testing and measurement criteria. Overdue account may utilize a time period to specify an operational definition such as any account over thirty days past due. Another method to operationally define overdue account may also use amount on account to classify accounts overdue, such as an account is overdue when it surpasses one hundred dollar balance. Both utilize different measurement types to provide tangible understanding. In looking at First line supervisor, a specific measurement type that could be used to construct an operational definition might look at the number of employees the supervisor oversees: a first line supervisor oversees a ten person team. A further specifying measurement for first line supervisor could involve the placement in the organization, for example, a first line supervisor is the first layer of leadership within the liability claims team. Line management could utilize measurement types similar to first line supervisor. In fact, line management could be a construct with first line supervisor being a concept of first line management. An operational definition that would provide a specified measurement characteristic might be as follows: the supervisor of first line supervisors is known as line management. Line management could also be defined with a measure of the type, number and budgetary size of equipment within their purview of responsibility. An example might be: a first line supervisor manages budgets greater than one million dollars annually in the manufacturing department with at least ten cotton gins and four first line supervisors. Again, definitions are specified and measurable. When we define constructs with operational definitions we would use concepts that make up the constructs. For example employee morale is abstract and not something tangible. To operationally define this construct you would use a concept that is defined and measurable to help develop the research conceptualization and understanding; for example employee morale is made up of the Franklin Covey survey given annually with a one through ten rating scale with one being the worst score to ten being the best. Another concept that might provide an operational definition is the salary adjustment increase developed from the annual performance review. These concepts provide measurable and specified value defining the construct. Assembly line and leadership may use concepts such as those defined above by being made up of first line supervisor, line manager. An operational definition for each of these constructs could be articulated by indicating that an assembly line or leadership is made up of four first line supervisors and one front line manager. Ethical standards could be operationally defined by including concepts by using a measurement of historical record of maintaining one hundred percent compliance of organizational policy or perfect attendance. A definition might be defined in this way, Ethical standards include members who have achieved a one hundred percent attendance for five years and have a perfect compliance record for five years. The two concepts in the construct are attendance and guideline compliance. A more abstract construct is Union Democracy. A measurement concept that would provide meaning to the construct could be political affiliation. A second could be workforce labor union. In both concepts one would specify the political affiliation or the type of union that makes up a Union democracy.
Hypothesis Testing (3.7) Cooper et al (2014, p. 58) define a hypothesis as “statements in which we assign variables to cases.” In the case of the firm that experiences frequent misrouted or dropped calls, a number of hypotheses may be tested in order to discover the cause or causes of the issues. Hypotheses provide the study with direction; limit the scope of the research; determines the effective form of research design; and frame the organization of the conclusion (Cooper et al, 2014). In the case of the firm, effective hypotheses may include: 1. Callers experience more dropped or misrouted calls during periods of heavy call volume. 2. Customers experience more misplaced or dropped calls during inclement weather (e.g., rain, snow, severe cold) than on days when the weather is benign. 3. An increase in visitor and customer traffic in the main office leads to an increase in the number of misplaced or dropped calls. 4. The receptionist is distracted by his hearing aid. 5. The receptionist is under 3 months of unsatisfactory performance due to tardiness. The “double movement of reflective thought” is the process in which a fact (e.g., the office’s experience of dropped or misplaced calls) is explained by an induced hypothesis; this hypothesis is then tested by deduction (Cooper et al, 2014). The first hypothesis suggests that the misplaced calls are related to the call volume experienced in a given period. In testing this hypothesis, one may examine the following deduction: calls fielded during a period of increased call volume will be dropped or misplaced. Concerning the second hypothesis, one may deduce that inclement weather results in poor call service; and poor weather results in more complaints concerning dropped or misplaced calls. The third hypothesis may be tested by the following deduction: above normal office traffic will result in misplaced or dropped calls. By the process of deduction, one may be able to test whether the hypotheses explain the occurrence of the sudden surge of dropped and misplaced calls (Cooper et al, 2014). An illustration of the forth and fifth hypotheses is found below. [pic]
Suggested Research for Apple iPad Application (App) Approval (4.4) An industry in constant boom is the smartphone and mobile devices such as iPads. One of the most popular platforms in mobile devices is the Apple iOS, with a marketplace offering hundreds of thousands of software applications (apps). The App Store offers a vast selection for a growing industry, with great revenue opportunities for Apple, developers and entrepreneurs. According to Cooper and Schindler (2014), the value of applied research is relevant to the added revenues or reduce expenses Therefore, all mobile marketing objectives should be tracked and considered for actionable decisions. As an app developer, rudimentary information and data are key to support the validity and use of such software tools. Preliminary testing and other forms of primary data collection involving both end-user and developers should be required in support of an iPad app submission for Apple’s consideration, approval, and use. In addition to functionality and creative elements of an app, developers should also consider providing data pertinent to patterns of use in order to justify its usefulness and value. Besides technical requirements needed for deployment, and legal and ethical aspects addressed, metrics are critical to showcase the potential success of the app. Metrics and information can be obtain from a series of offline and online sources, to include the use of web based graphical user interface that displays data through the use of tables and charts. It is important for the developers to be able to showcase in their proposal submission a report capturing research findings such as actionable app insights, where data can help anticipate challenges, track production and maintenance costs, and help anticipate apps ROI. By understanding user’s drivers for downloading and using the app, the developers will be able to quantify and understand consumers’ behaviors and optimize the experience for Apple iPad users.
TJX Data Mining Strategies (5.4)

In TJX’s breach of security they only had the option to continually check for credit card fraud would be through pattern discovery. TJX runs the risk of losing many customers due to not having the proper checking capabilities set up throughout their entire IT system. As explained in this chapter; data-mining tools can be programmed to sweep regularly through databases and identify previously hidden patterns (Cooper et al, 2014). By regulating the times the databases are combed through that will give TJX the capabilities to protect their customers. With proper training of how to use the electronic databases coupled with data-mining these are multiple ways of protecting the consumer. Predicting trends and behavior is another aspect in data-mining that could assist TJX in securing their customers privacy. Through the data-mining process TJX could send mailings out to their customers to inform them that they are on high alert for credit card fraud. Informing the public of this issue is putting them on high alert in order to slow down the fraud issue. Now the customers of TJX will check their statements and be more proactive if they see unusual charges. Predicting trends and behaviors can be done by using software to find the exact marketing programs that attract high-margin, low-risk customers (Cooper et al, 2014). Taking that information will allow a fraud detection team to know what kind of purchases to be suspicious of. With the world evolving and new threats on credit card usage arising; companies such as TJX have to find innovative ways to protect their customer. At the end of the day that only party losing money would be TJX. They run the risk of losing customers, reimbursable money they would have to give back to the customer who suffered through credit card fraud and the image they are putting out for the public to see how they treat their consumer base.

References
Cooper, D. R., & Schindler, P. S. (2014). Business research methods (12th ed.). New York, NY: McGraw-Hill. [pic]