Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference

The Effect of Individual Differences, Tasks, and Decision Models on User Acceptance of Decision Support Systems

Past studies suggested that decision support systems (DSS) must be an “enabling” system aiming to enhance users’ capabilities and to leverage their skills and intelligence. This suggests that users be the center of DSS and users’ characteristics be an important factor of explaining their DSS acceptance behavior. Since DSS are aimed to work in semi-structured and unstructured task environment, perceived task complexity can be used to explain users’ willingness to accept DSS. Further, several studies also used decision models for investigating users’ DSS acceptance behavior. We argue that nature of DSS (based on their underlying decision models) and its interaction with individual differences also play important roles on users’ DSS acceptance behavior. With the conjecture that users’ DSS acceptance behavior directly affects the DSS usage and DSS success, our research question focuses on how do individual differences influence users’ DSS acceptance behavior with consideration of task characteristics and nature of the DSS. The contribution of this paper is multifold. First, we extend the existing understanding of effects of individual differences on users’ DSS acceptance behavior. Second, we extend two major measurements of cognitive styles (GEFT - Group Embedded Figures Test and MBTI - Myers-Briggs Type Indicator) for individual differences in the context of DSS. Third, we investigate multiple task complexities and multiple DSS models. Hypotheses are developed and will be tested with an experiment of 300 plus subjects

Design of Data-Driven Decision Support Systems for Business Process Standardization

Increasingly dynamic environments require organizations to engage in business process standardization (BPS) in response to environmental change. However, BPS depends on numerous contingency factors from different layers of the organization, such as strategy, business models (BMs), business processes (BPs) and application systems that need to be well-understood (“comprehended”) and taken into account by decision-makers for selecting appropriate standard BP designs that fit the organization. Besides, common approaches to BPS are non-data-driven and frequently do not exploit increasingly avail-able data in organizations. Therefore, this thesis addresses the following research ques-tion: “How to design data-driven decision support systems to increase the comprehen-sion of contingency factors on business process standardization?”. Theoretically grounded in organizational contingency theory (OCT), this thesis address-es the research question by conducting three design science research (DSR) projects to design data-driven decision support systems (DSSs) for SAP R/3 and S/4 HANA ERP systems that increase comprehension of BPS contingency factors. The thesis conducts the DSR projects at an industry partner within the context of a BPS and SAP S/4 HANA transformation program at a global manufacturing corporation. DSR project 1 designs a data-driven “Business Model Mining” system that automatical-ly “mines” BMs from data in application systems and represents results in an interactive “Business Model Canvas” (BMC) BI dashboard to comprehend BM-related BPS con-tingency factors. The project derives generic design requirements and a blueprint con-ceptualization for BMM systems and suggests an open, standardized reference data model for BMM. The project implements the software artifact “Business Model Miner” in Microsoft Azure / PowerBI and demonstrates technical feasibility by using data from an educational SAP S/4 HANA system, an open reference dataset, and three real-life SAP R/3 ERP systems. A field evaluation with 21 managers at the industry partner finds differences between tool results and BMCs created by managers and thus the po-tential for a complementary role of BMM tools to enrich the comprehension of BMs. A further controlled laboratory experiment with 142 students finds significant beneficial impacts on subjective and objective comprehension in terms of effectiveness, efficiency, and relative efficiency. Second, DSR project 2 designs a data-driven process mining DSS “KeyPro” to semi-automatically discover and prioritize the set of BPs occurring in an organization from log data to concentrate BPS initiatives on important BPs given limited organizational resources. The project derives objective and quantifiable BP importance metrics from BM and BPM literature and implements KeyPro for SAP R/3 ERP and S/4 HANA sys-tems in Microsoft SQL Server / Azure and interactive PowerBI dashboards. A field evaluation with 52 managers compares BPs detected manually by decision-makers against BPs discovered by KeyPro and reveals significant differences and a complemen-tary role of the artifact to deliver additional insights into the set of BPs in the organiza-tion. Finally, a controlled laboratory experiment with 30 students identifies the dash-boards with the lowest comprehension for further development. Third, OCT requires organizations to select a standard BP design that matches contin-gencies. Thus, DSR project 3 designs a process mining DSS to select a standard BP from a repository of different alternative designs based on the similarity of BPS contin-gency factors between the as-is process and the to-be standard processes. DSR project 3 thus derives four different process model variants for representing BPS contingency factors that vary according to determinant factors of process model comprehension (PMC) identified in PMC literature. A controlled laboratory evaluation with 150 stu-dents identifies significant differences in PMC. Based on laboratory findings, the DSS is implemented in the BPM platform “Apromore” to select standard BP reference mod-els from the SAP Best Practices Explorer for SAP S/4 HANA and applied for the pur-chase-to-pay and order-to-cash process of a manufacturing company

Evaluation of key value drivers as a decision support tool for strategy implementation in BHP Billiton Manganese

This study evaluated the use of Key Value Drivers as a decision support tool for strategy implementation in BHP Billiton Manganese. The evaluation methods used in this study were subjective and were based on perception data collected from BHP Billiton Manganese Management. Three data collection methods were used, namely, survey questionnaire, archival search and interviews. The results obtained indicated that BHP Billiton Manganese managers perceive Key Value Drivers to be an effective decision support tool for strategy implementation, however the current Microsoft Excel model that has evolved over the past decade is perceived to be difficult to maintain with respect to data management and the support that is offered to users in the form of training material and on-line help is limited. The study indicated that Key Value Drivers as currently used in BHP Billiton Manganese, are an important integrator for a number of business processes such as Planning, Performance Management, Business Improvement and Management Information Systems. At a practical level, the study provided a method for identification and ranking of Key Value Drivers and a subjective evaluation process that can be used to get user input in design and implementation of management information systems. At a theoretical level, the study has shown that the relevance of Decision Support Systems and Value Based Management approaches still persists in contemporary managerial decision-making and that there is potential to use modern technologies such as Business Intelligence platforms to support these legacy systems. The empirical findings of this study were in general supportive of what could be expected based on the literature review covering Decision Support Systems, Key Value Drivers, Business Intelligence and Information Systems’ Evaluation Approaches. The Business Intelligence implementation project that is currently underway will benefit from the feedback generated by this study, particularly by ensuring that the two key shortcomings of the current KVD model are addressed. The study was a cross-sectional study limited to BHP Billiton Manganese. The study can be replicated in other Customer Sector Groups or repeated in BHP Billiton Manganese to create a longitudinal profil

A framework for knowledge discovery within business intelligence for decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Business Intelligence (BI) techniques provide the potential to not only efficiently manage but further analyse and apply the collected information in an effective manner. Benefiting from research both within industry and academia, BI provides functionality for accessing, cleansing, transforming, analysing and reporting organisational datasets. This provides further opportunities for the data to be explored and assist organisations in the discovery of correlations, trends and patterns that exist hidden within the data. This hidden information can be employed to provide an insight into opportunities to make an organisation more competitive by allowing manager to make more informed decisions and as a result, corporate resources optimally utilised. This potential insight provides organisations with an unrivalled opportunity to remain abreast of market trends. Consequently, BI techniques provide significant opportunity for integration with Decision Support Systems (DSS). The gap which was identified within the current body of knowledge and motivated this research, revealed that currently no suitable framework for BI, which can be applied at a meta-level and is therefore tool, technology and domain independent, currently exists. To address the identified gap this study proposes a meta-level framework: - ‘KDDS-BI’, which can be applied at an abstract level and therefore structure a BI investigation, irrespective of the end user. KDDS-BI not only facilitates the selection of suitable techniques for BI investigations, reducing the reliance upon ad-hoc investigative approaches which rely upon ‘trial and error’, yet further integrates Knowledge Management (KM) principles to ensure the retention and transfer of knowledge due to a structured approach to provide DSS that are based upon the principles of BI. In order to evaluate and validate the framework, KDDS-BI has been investigated through three distinct case studies. First KDDS-BI facilitates the integration of BI within ‘Direct Marketing’ to provide innovative solutions for analysis based upon the most suitable BI technique. Secondly, KDDS-BI is investigated within sales promotion, to facilitate the selection of tools and techniques for more focused in store marketing campaigns and increase revenue through the discovery of hidden data, and finally, operations management is analysed within a highly dynamic and unstructured environment of the London Underground Ltd. network through unique a BI solution to organise and manage resources, thereby increasing the efficiency of business processes. The three case studies provide insight into not only how KDDS-BI provides structure to the integration of BI within business process, but additionally the opportunity to analyse the performance of KDDS-BI within three independent environments for distinct purposes provided structure through KDDS-BI thereby validating and corroborating the proposed framework and adding value to business processes

Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework

Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework

The effect of faculty performance measurement systems on student retention

Institutions of higher learning have been tracking student course-drop rates as a measure of student success along with faculty performance data. However, there is a lack of understanding as to how faculty performance data influences drop rates. The purpose of this study was to determine whether faculty knowledge of performance data creates a difference in drop rates. This study combined theories of performance measurement, decision support, self-determination theory (SDT), and personal decision making (PDM) as a conceptual foundation that linked faculty knowledge to student success. The specific research question addressed if data can be used to assist faculty efforts in reducing student attrition. This experimental longitudinal study tested the effect of faculty knowledge of personal performance measures on student course-drop rates. A sample of 32 subjects from a major university were randomly selected and assigned to equivalent-groups that included an experimental group, which received performance feedback and instruction, and an uninformed control group. Paired sample t-tests indicated a significant 32.8% reduction in student attrition for faculty in the experimental group, compared to a 10.3% increase in attrition observed for the control group faculty. Results suggest that providing faculty access to performance data via a decision support system will result in a reduction of student course drop rates. The key social value for this study is to provide a blueprint in collecting, structuring, and disseminating data that assist faculty and institutions in addressing student persistence. Students who persist in their courses have a greater potential of completing their studies and thus gaining access to better paying careers, higher levels of self-esteem, and an overall improved quality of life

An Investigation of the Managerial Use of Mobile Business Intelligence

As a new trend in business intelligence (BI), mobile BI has been gaining increasing adoption by managers. However, there is little academic research about the managerial use of mobile BI. Adopting the key constructs of Task-Technology Fit theory and the Unified Theory of Acceptance and Use of Technology as the theoretical lens, this exploratory study aims to deliver a preliminary understanding on why and how managers use mobile BI, from both the managers’ and the vendor’s perspectives. A case study was conducted with a large government authority whose mobile BI vendor is an industry leader worldwide. Semi-structured interviews were carried out with seven senior managers from this organization and the vendor. Through discussing the reasons why managers use mobile BI and their use patterns, a series of emergent propositions are drawn. The empirical results from this study not only contribute to this currently underexplored area of mobile BI, but also help enable the industry to make mobile BI products that better suit managers’ needs. Available at: https://aisel.aisnet.org/pajais/vol10/iss3/4