Managing Decision Making

Why is End User Development of spreadsheet decision support systems (DSS) an integral part of business today?
• Errors in Spreadsheet design (Your answer have to and only have to according to this )
Word limit 400
Using at least three academic references.

Only Answering these three parts:

1. Discussion
2. Recommendation
3. Conclusion

Spreadsheets are very crucial in organizations due to the role they play in informed managerial decision-making. Businesses, private and public sectors utilize spreadsheets as tools in documenting files and decision-making. In large organizations, complex spreadsheets are used that usually contain errors that sometimes may be difficult to detect (Papandrea, 2017). The argument advanced by Goswami, Hock Chuan & Hee Woong (2008) is that spreadsheet errors cause serious problems in academics and organizations with huge cost implications. Therefore, understanding these errors would make it easier for users of electronic spreadsheets to avoid using data with errors. This paper discusses the common types of errors that are found in spreadsheet design. Mechanical, logic, omission, and modeling errors are the major types of spreadsheet design errors that have been a significant problem to managerial decision-making.
Types of Errors
Spreadsheet design errors could either be data errors or bottom line errors. Data errors often yield an error message or in quantitative data, produce the wrong result in the spreadsheet. Bottom line errors are those that are formed in the initialization process, and cause the other data related to it to show the wrong results. These errors are further divided into two categories of spreadsheet errors; qualitative and quantitative errors (Callahan, 2002).
Quantitative Errors
Kadijevich asserts that spreadsheet design errors in quantitative data include the selection of wrong variables when preparing spreadsheets (2012). Such errors also arise from the wrong initialization of data in spreadsheets. In the correlation process, the wrong relation of variables in the spreadsheet results in quantitative errors that yield wrong spreadsheet results.

As Janvrin (2008) notes, quantitative errors give an immediate result while qualitative errors are not highlighted in the immediate result. Mistyped value in cells that give data error in the immediate result is an example of a quantitative error. Such errors are of three types, mechanical, logic, and omission errors (Janvrin, 2008).
Mechanical errors are caused by simple mistakes such as mistyping of values, signs or pointing the wrong cells. An example is the cell error message that relates to wrong entry to a specific cell in the spreadsheet. According to Dobell, Herold & Buckley (2018), cell errors consist of 13% of all spreadsheet errors identified. Cell errors may cause a waste of the whole spreadsheet, especially if the data is interconnected. Callahan (2002) asserts that a mistyped value in a cell causes the #VALUE! error alert message that shows the value in the highlighted cell is wrongly typed. The error is easily identified if entered close to where the error message appears; however, if the error is on a different page or affects connected spreadsheets, it wreaks havoc in the spreadsheets.
Logic errors, on the other hand, are caused by using the wrong formulas in spreadsheets and are usually serious problems that are difficult to detect. An example of a logic error in spreadsheet design is the use of variable costs only without including fixed costs in computations (Kadijevich, 2012:144).
Lastly, the omission errors arise from missed entries and are the most difficult to detect since there is no error message yielded when entering data for reports (Janvrin, 2008: 440). A good example of omission error is the omission of marketing costs in a spreadsheet that aims to compute costs of sales.

Qualitative Errors
Qualitative errors are errors in the spreadsheet design that do not show immediate quantitative errors but yield such errors later. According to Janvrin (2008: 441), a spreadsheet design error includes errors in which a value used on one spreadsheet reflects the wrong value, which affects the rest of computations in the spreadsheet. A good example is when a discount of 8% is used instead of a discount of 10% on sales. When the discount rate changes and the spreadsheet is not designed to reflect the change, then, a qualitative error occurs.
Spreadsheets errors also result when the spreadsheet modeling is done wrongly. According to Kadijevich (2012), errors occur when a spreadsheet modeler fails to include certain variables that influence a given parameter. For instance, in preparing a spreadsheet on coffee production, a modeler may forget important cost areas such as the fixed costs of the business space. In this case, the spreadsheet design error results in wrong calculations in the final computation of data. The failure to use all the variables that relate to costs of coffee production is what results in this type of error. Moreover, the selection of the wrong function to be used in the computation of various reports leads to wrong initialization of variables. These errors yield the wrong results in the final reports and seriously affect managerial decision-making (Kadijevich, 2015: 143).
Spreadsheets have formulas from which a modeler is to select when compiling data. Some of the formulae in electronic spreadsheets are wrong and mislead modelers without knowledge of the errors, resulting in the wrong computation in the end (Callahan, 2002). Errors that arise from the use of the wrong formulas in spreadsheet modeling result in erroneous results. Kadijevich (2012) notes that when a wrong formulae is used in data computation, a wrong report is produced. When preparing a spreadsheet, the user may relate variables wrongly or use formulas that do not reflect the relationship between variables. For instance, in a hotel service, a modeler designing a spreadsheet may put costs that do not relate to the volume of service. The use of the number of guests does not necessarily indicate the volume of services since different guests order and consume different services, with others asking for take-away. Such a design error would lead to wrong data and reports (Kadijevich, 2012:143).
The common types of errors include mechanical, logic, omission, and modeling errors. Understanding these design errors would help spreadsheet users find ways to avoid them when entering data and using spreadsheets to create reports. While some of the errors are not easy to detect, the knowledge of their types would keep users alert and ensure that the right data and variables are entered in spreadsheets.

Callahan, T 2002, ‘Block That Spreadsheet Error’, Journal Of Accountancy, 194, 2, pp. 59-63, Business Source Complete, EBSCOhost, viewed 4 April 2018.
Dobell, E, Herold, S, & Buckley, J 2018, ‘Spreadsheet Error Types and Their Prevalence in a HealthcareContext’, Journal Of Organizational & End User Computing, 30, 2, pp. 20-42, Library, Information Science & Technology Abstracts, EBSCOhost, viewed 4 April 2018.
Goswami, S, Hock Chuan, C, & Hee Woong, K 2008, ‘The Role of Visualization Tools in Spreadsheet Error Correction from a Cognitive Fit Perspective’, Journal Of The Association For Information Systems, 9, 6, pp. 321-343, Business Source Complete, EBSCOhost, viewed 4 April 2018.
Janvrin, DJ 2008, ‘Detecting Spreadsheet Errors: An Education Case’, Issues In Accounting Education, 23, 3, pp. 435-454, Business Source Complete, EBSCOhost, viewed 4 April 2018.
Kadijevich, DM 2012, ‘Examining Errors in Simple Spreadsheet Modeling from Different ResearchPerspectives’, Journal Of Educational Computing Research, 47, 2, pp. 137-153, Professional Development Collection, EBSCOhost, viewed 4 April 2018.
Papandrea, D 2017, ‘budgeting outside the box: Using sophisticated tech tools to up the financial planning and budgeting game’, University Business, 20, 12, pp. 43-46, Professional Development Collection, EBSCOhost, viewed 4 April 2018.
Powell, S, Baker, K, & Lawson, B 2009, ‘Errors in Operational Spreadsheets’, Journal Of Organizational & End User Computing, 21, 3, pp. 24-36, Library, Information Science & Technology Abstracts, EBSCOhost, viewed 4 April 2018.

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