Browsing by Author "Parbanath, Steven."

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Computer-based productivity estimation of academic staff using the fuzzy analytic hierarchy process and fuzzy topsis method.
(2014) Parbanath, Steven.; Maharaj, Manoj Sewak.
Universities generally use a human-intensive approach such as peer evaluations, expert judgments, group interviews or a weighting system to estimate academic productivity. This study develops an algorithmic approach by integrating the fuzzy Multi-Criteria Decision Making (MCDM) and the fuzzy TOPSIS methods to estimate productivity of academic staff at tertiary institutions. Currently, evaluations are done in the conventional manner and as a result, the outputs are difficult to quantify. There are no standard methods in evaluating the outputs and the estimates are therefore hard to validate. It is therefore suggested that a data intensive approach (also referred to as algorithmic approach) be adopted. An algorithmic approach is empirical and will produce results that are easily quantifiable. The algorithmic approach allows for the IS Principles of data collection, processing, analysis and interpretation to be easily applied. If an algorithmic approach were adopted, it would generally revolve around the numeric-value approach, which produces a precise measure of productivity. Recently however, the software engineering domain had to also consider non-numeric attributes (also referred to as linguistic expressions) such as very low, low, high and very high for productivity estimation (Odeyale et al., 2014). The imprecise nature of these attributes constitutes uncertainty in their interpretation and therefore could not be measured or quantified appropriately in the past. It is now possible to do so using fuzzy logic and fuzzy sets. Since academic departments are constantly faced with imprecision and uncertainty, an algorithmic fuzzy-based decision model is the most suitable approach that can be used to estimate productivity. The nature of duties performed by academic staff lends itself more efficiently to a qualitative rather than a quantitative evaluation (Chaudhari et al., 2012). These qualitative evaluations are reliant on human judgment and could be described using linguistic expressions such as weak, average, good and excellent (Khan et al., 2011). In this study, a fuzzy MCDM method called Fuzzy Analytic Hierarchy Process (FAHP) is used to estimate productivity of academic staff. Choosing the most preferred alternative, ranking and selection will be done using the fuzzy TOPSIS method. The Design Science Research Methodology (DSRM) was used to develop a fuzzy-based productivity estimation system using these two methods. The results of the study indicated that the fuzzy-based system produced results that were more reliable than conventional methods. Future research should however explore how fuzzy logic and fuzzy set theory could be integrated into other productivity estimation techniques such as the DEA and SAW models.
Personal information security : legislation, awareness and attitude.
(2011) Parbanath, Steven.; Maharaj, Manoj Sewak.
Ecommerce refers to the buying and selling of products and services electronically via the Internet and other computer networks (Electronic Commerce 2011). The critical components of ecommerce are a well designed website and a merchant account for payment by the customer (Ecommerce critical components 2008). Merchants that sell their products and services via the Internet have a competitive edge over those that do not. It is therefore becoming common practice for both small and large business to transact electronically. With the vast opportunities, new risks and vulnerabilities are introduced. Consumers are reluctant to transact electronically because of the fear of unauthorized access and interception of confidential information (Online Banking Concerns 2011). Other fears include the changing of data with malicious intent, denial of use, hacking, deliberate disclosure of confidential information and e-mail associated risks (Safeena, Abdulla & Date 2010). The use of technology such as encryption and decryption has not adequately addressed these problems because fraudsters have found new and sophisticated methods of attaining consumer information illegally. Phishing is one such method. Phishing results in identity theft and financial fraud when the fraudster tricks the online users into giving their confidential information like passwords, identity numbers, credit card number and personal information such as birthdates and maiden names. The fraudster will then use the information to impersonate the victim to transfer funds from the victim‟s account or use the victim‟s information to make purchases (Srivastava 2007). Since 2002, many laws passed in South Africa have attempted to allay fears so that consumers can conduct business electronically with confidence. The following legislation aims to protect consumers: - The Electronic Communications and Transactions Act (Republic of South Africa 2002). - The Consumer Protection Act (Republic of South Africa 2008). - The Protection of Personal Information Bill which is expected to be passed in 2011 (Republic of South Africa 2009). This research aims to examine the extent to which these legislation can address the security concerns of consumers. The researcher is also interested in ascertaining how knowledgeable consumers are on these legislation and what their attitudes are towards their personal information security.