Using Network Analysis to Improve Decision Making for Partner Selection in Inter-Organizational Networks

2013 ◽  
Vol 4 (3) ◽  
pp. 26-39
Author(s):  
Laura Ponisio ◽  
Pascal van Eck ◽  
Lourens Riemens
Keyword(s):  
Decision Making ◽  
Network Analysis ◽  
Best Practices ◽  
Ad Hoc ◽  
Decision Processes ◽  
Decision Makers ◽  
Development Projects ◽  
It Professionals ◽  
Analysis Techniques ◽  
Development Levels

Professionals in decision making roles are often faced with the problem of choosing partners for closer cooperation, for instance, to start new joint IT development projects or for harvesting best practices. The large amounts of information involved in these decision processes obscure possibilities, and therefore choices are made ad hoc. In this article, the authors present an approach that uses concrete data and network analysis to support decision makers in processing and understanding this information. Central in the authors’ approach are questionnaires capturing aspired and current development levels of the processes of the cooperating organizations and graphs generated using network analysis techniques. The advantage of the authors’ approach, which they validated via expert interviews, is that results are semi-automatically translated to visualizations; which in turn offer an overall view of the current and aspired situation in the network without losing the ability to pinpoint particular, individual processes of interest. This, in turn, enables IT professionals to make better decisions.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

Explaining monotonic ranking functions

2020 ◽  
Vol 14 (4) ◽  
pp. 640-652
Author(s):  
Abraham Gale ◽  
Amélie Marian
Keyword(s):  
Decision Making ◽  
Selection Process ◽  
A Priori ◽  
Data Distribution ◽  
Synthetic Data ◽  
Decision Processes ◽  
Decision Makers ◽  
Ranking Functions ◽  
High School Admissions ◽  
Parameter Values

Ranking functions are commonly used to assist in decision-making in a wide variety of applications. As the general public realizes the significant societal impacts of the widespread use of algorithms in decision-making, there has been a push towards explainability and transparency in decision processes and results, as well as demands to justify the fairness of the processes. In this paper, we focus on providing metrics towards explainability and transparency of ranking functions, with a focus towards making the ranking process understandable, a priori , so that decision-makers can make informed choices when designing their ranking selection process. We propose transparent participation metrics to clarify the ranking process, by assessing the contribution of each parameter used in the ranking function in the creation of the final ranked outcome, using information about the ranking functions themselves, as well as observations of the underlying distributions of the parameter values involved in the ranking. To evaluate the outcome of the ranking process, we propose diversity and disparity metrics to measure how similar the selected objects are to each other, and to the underlying data distribution. We evaluate the behavior of our metrics on synthetic data, as well as on data and ranking functions on two real-world scenarios: high school admissions and decathlon scoring.

Integrating OLAP/SOLAP in E-Business Domains

2011 ◽  
Vol 24 (3) ◽  
pp. 45-60
Author(s):  
Ben Ali ◽  
Samar Mouakket
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
United Arab Emirates ◽  
Decision Makers ◽  
Job Seekers ◽  
Job Market ◽  
Analysis Techniques ◽  
Analytical Processing ◽  
User Friendly

E-business domains have been considered killer domains for different data analysis techniques. Most researchers have examined data mining (DM) techniques to analyze the databases behind E-business websites. DM has shown interesting results, but this technique presents some restrictions concerning the content of the database and the level of expertise of the users interpreting the results. In this paper, the authors show that successful and more sophisticated results can be obtained using other analysis techniques, such as Online Analytical Processing (OLAP) and Spatial OLAP (SOLAP). Thus, the authors propose a framework that fuses or integrates OLAP with SOLAP techniques in an E-business domain to perform easier and more user-friendly data analysis (non-spatial and spatial) and improve decision making. In addition, the authors apply the framework to an E-business website related to online job seekers in the United Arab Emirates (UAE). The results can be used effectively by decision makers to make crucial decisions in the job market of the UAE.

scholarly journals Strategic investment decision-making practices in large manufacturing companies

2020 ◽  
Vol 28 (4) ◽  
pp. 633-653 ◽  
Author(s):  
Fadi Alkaraan
Keyword(s):  
Decision Making ◽  
Investment Decision ◽  
Decision Makers ◽  
Control Mechanisms ◽  
Manufacturing Companies ◽  
Strategic Investment ◽  
Content Type ◽  
Investment Projects ◽  
Analysis Techniques ◽  
Investment Decision Making

Purpose This paper aims to examine the adoption of conventional and emergent analysis techniques in Strategic Investment Decision-Making (SIDM) practices in large UK manufacturing companies. It aims to update the current knowledge on SIDM practices in large manufacturing companies. The research question underlying this study: Are recently developed analysis techniques (i.e. those that aim to integrate strategic and financial analyses) being used to evaluate strategic investment projects? Design/methodology/approach The research evidence underpinning this study was made up of primary and secondary data, quantitative and qualitative. Firstly, a survey consisting of a mailed formal standard questionnaire was conducted where each respondent is required to answer the same questions based on the same system of coded responses. Secondly, qualitative data was collected using the annual reports of selected companies. Disclosures were used as supplementary source of information using the explanatory notes and parenthetical disclosures accompanying companies’ financial reporting. Sources for these disclosures included management discussions, analyses of company strategy and risk and forward-looking reports regarding future performance and growth opportunities (such as mergers and acquisitions activities). Accordingly, companies’ disclosures were used in this study as an alternative method to semi-structured interviews to collect qualitative data. More recently, companies such as Rio Tinto have prepared strategic annual reports for 2017 against the UK Corporate Governance Code (version 2016). Findings The choice and use of financial analysis techniques and risk analysis techniques depend on the type of project being evaluated. Decision makers in large UK companies do not appear to use emergent analysis techniques widely. Pre-decision control mechanisms have significant influence on SIDM practices. This includes the changes of internal and external contextual factors, including organisational culture, organisational strategies, financial consideration, comprising formal approval governance mechanisms, regulatory and other compliance policies interact with companies’ internal control systems. Companies incorporate non-financial factors alongside quantitative analysis of strategic investments opportunities. Energy efficiency and carbon reduction are key imperatives of companies’ environmental management. These factors viewed by decision makers as significant factors relevant for compliance with legislation as well as maintaining companies’ legitimacy issues, sustainable business, experience with new technology and improved company image. Research limitations/implications High risk, ambiguity and complexity are key characteristics embedded in SIDM processes. Macroeconomic issues remain crucial factors in scanning and screening investment opportunities, as reported by this study. The early stage of SIDM processes requires modelling under macroeconomic scenarios and assumptions of both internal and external parameters. Key assumptions include: projections of economic growth; commodity prices and exchange rates, introduction of technological and productivity advancements; cost and supply parameters for major inputs. SIDM practices rooted on comprehensive knowledge and experience of the industry and markets to draw subjective judgements about the riskiness of prospective projects, but these are rarely formalized into their SIDM processes. Findings of this study, however, remain within the context of UK companies. This study has its own limitations due to its time, location, respondents and sample selection, the size and the sector of the selected companies and questions addressed. Findings of this study raise a call for future research to examine SIDM processes in different settings to explore the relative impact of various organisational control mechanisms on SIDM practices. Also, to examine the influence of contextual factors (such as national culture, political, legal and social factors) on organisational control mechanisms. SIDM practices and processes have received significant attention from researchers, yet there is a lack of evidence in the literature about how companies approach strategic decision-making regarding divestments of some of their strategic investments. This type of strategic decision-making is not less important than other types of SIDM practices. Practical implications SIDM practices reflect the art and science of steering and controlling organisational resources to achieve a desired strategy. To understand the factors that shape SIDM practices and align them to organisational strategy, more attention is required to the choice and design of pre-decision controls and to the important role of strategic management accounting tools over the more traditional financial analysis techniques that have formed the focus of much prior empirical research. Social implications Key environmental issues viewed by decision makers as significant factors relevant for compliance with legislation as well as maintaining companies’ legitimacy issues and company image. Originality/value Despite their perceived importance in this study, quantitative accounting controls may fail to connect with the kind of investment decision-making required to bring strategic success. Indeed, it has been widely noted that financial evaluation techniques are inadequate for assessing strategic investment proposals; they can only function as a guideline, as SIDM practices involve so many uncertainties, risks and judgements. A key insight from this study is that the achievement of integration between the firm’s strategic investment projects and the overall organizational strategy forms a critical pre-decision control on managerial behaviour at an early stage in SIDM practices. As many strategic investment decisions are one-off, non-repeatable decisions, the information needed to support their evaluation is likely to be similarly unique. Sound SIDM practices require the support of a large amount of varied information, a significant proportion of which is collected and analysed prior to potential capital investment projects being considered, such as information related to strategic goal setting, risk-adjusted hurdle rates and the design of appropriate organisational decision hierarchies.

Medical Informatics and Clinical Decision Making: The Science and the Pragmatics

1991 ◽  
Vol 11 (4_suppl) ◽  
pp. S2-S14 ◽  
Author(s):  
Edward H. Shortliffe
Keyword(s):  
Decision Making ◽  
Medical Informatics ◽  
Ad Hoc ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Decision Makers ◽  
Medical Decision ◽  
Normative Models ◽  
Outcomes Management ◽  
Collaborative Efforts

There are important scientific and pragmatic synergies between the medical decision making field and the emerging discipline of medical informatics. In the 1970s, the field of medicine forced clinically oriented artificial intelligence (AI) researchers to develop ways to manage explicit statements of uncertainty in expert systems. Classic probability theory was considered and discussed, but it tended to be abandoned because of complexities that limited its use. In medical AI systems, uncertainty was handled by a variety of ad hoc models that simulated probabilistic considerations. To illustrate the scientific interactions between the fields, the author describes recent work in his laboratory that has attempted to show that formal normative models based on probability and decision theory can be practically melded with AI methods to deliver effective advisory tools. In addition, the practical needs of decision makers and health policy planners are increasingly necessitating collaborative efforts to develop a computing and communications infrastructure for the decision making and informatics communities. This point is illustrated with an example drawn from outcomes management research.

TOPSIS vs. VIKOR

2020 ◽  
pp. 225-250
Author(s):  
Semra Erpolat Taşabat ◽  
Tuğba Kıral Özkan
Keyword(s):  
Decision Making ◽  
Human Development ◽  
Human Development Index ◽  
Decision Makers ◽  
Multi Criteria Decision Making ◽  
Vikor Method ◽  
Data Set ◽  
Development Index ◽  
Multidisciplinary Nature ◽  
Development Levels

Evaluating multiple criteria and selecting and/or ranking alternatives is called Multi Criteria Decision Making (MCDM). These methods which are considered important decision-making tools for decision makers due to their multidisciplinary nature have been developed over the years. As a result, there are many MCDM methods in the literature. In this chapter, TOPSIS and VIKOR, widely used in the literature, will be discussed. The major reason for examining these two methods is that the aggregating function used by both methods is similar because VIKOR method uses linear normalization and TOPSIS method uses vector normalization. The process of the methods is shown on a data set that includes the Human Development Index (HDI) indicators, which have been developed to measure the development levels of countries as well as the unemployment indicator. It was observed that the methods yielded similar results.

Deciding for Responsibility and Legitimation: Alternative Interpretations of Organizational Decision‐Making

2017 ◽  
Author(s):  
Nils Brunsson
Keyword(s):  
Decision Making ◽  
Local Governments ◽  
Empirical Studies ◽  
Decision Processes ◽  
Decision Makers ◽  
Organizational Decision Making ◽  
Empirical Validity ◽  
Organizational Action ◽  
National Governments

Recent studies have questioned the empirical validity of the equating of decision and choice and pointed at another role that organizational decisions sometimes play — the role of mobilizing organizational action, a role that requires less rationality than choice. But choice and mobilization are not the only roles of decision-making and decisions in organizations. This chapter argues that two additional roles exist — decisions may allocate responsibility and legitimacy to decision-makers and organizations. The chapter also considers how the different roles can explain the design of decision processes, the use of information and the number of decisions in organizations. The discussion is based on empirical studies of decision processes in such organizations: in local governments, national governments, and company boards. The eight decision processes studied concern city budgets, investments and disinvestments, and governmental programmes.

scholarly journals Developing a competitive intelligence strategy framework supporting the competitive intelligence needs of a financial institution’s decision makers

2016 ◽  
Vol 18 (2) ◽  
Author(s):  
Tanya Du Plessis ◽  
Mzoxolo Gulwa
Keyword(s):  
Decision Making ◽  
Financial Services ◽  
Qualitative Data ◽  
Numerical Data ◽  
Decision Makers ◽  
Competitive Intelligence ◽  
Strategy Development ◽  
Planning Tool ◽  
Analysis Techniques

Background: For competitive intelligence (CI) to have the greatest contribution to strategic management, CI professionals require an in-depth understanding of the CI needs of decision makers. CI professionals have to carefully plan how to best inform corporate decision-making.A strategy framework is a planning tool which can be used to explore ways to enhance an organisation’s strategic planning capabilities.Objective: To investigate the CI needs of a financial institution’s decision makers in order to develop a CI strategy framework. To present the strategy framework as a planning tool to CI professionals in the financial services industry as well as mapping the process of developing a planning tool, thereby enabling a financial institution’s CI capability to better meet the CI needs of decision makers.Method: The guiding paradigm of interpretivist research directed the research design of a single qualitative case study, using an inductive approach. Qualitative data analysis techniques were used, which included the use of numerical data, to develop a planning tool for CI professionals based on a thorough understanding of the CI needs of decision makers.Results: Decision makers place considerable value on CI in terms of its contribution to strategy development, decision-making, gaining advantage over competitors and enhancing the financial performance of the organisation. Relationships between concepts and patterns or trends that were identified and utilised to establish themes in the data resulted in a 12-point strategy framework.Conclusion: A financial institution’s CI capability can be enhanced to better meet the CI needs of the organisation’s decision makers when CI professionals carefully plan their approach of informing corporate decision-making. This paper presents a 12-point CI strategy framework as a planning tool for CI professionals.

Decision Making in Soviet Cities

1972 ◽  
Vol 66 (1) ◽  
pp. 38-52 ◽  
Author(s):  
B. Michael Frolic
Keyword(s):  
Decision Making ◽  
Ad Hoc ◽  
Planning Process ◽  
Municipal Government ◽  
Decision Makers ◽  
Group Interest ◽  
The Past ◽  
Party Members ◽  
Municipal Administrators ◽  
Made In

How are decisions made in Soviet cities? Who are the municipal decision makers? What kinds of decisions do they make? Is there a Soviet urban political system? This article attempts to answer these questions by focusing on four aspects of decision making in Soviet cities: budget formulation, the planning process, housing construction and allocation, and the staffing of key municipal posts.Urban autonomy has increased in the past decade, but Soviet municipalities are very much restricted in their decision making. Superior Party and governmental authorities continue to dominate the decision-making process and any decision made by municipal authorities can be vetoed by superior Party and governmental organs.Soviet municipal decision making is now being influenced by three tendencies: municipal administrators are acquiring more influence in municipal government and administration; the educational qualifications and professional expertise of city Party members are rising; ad hoc citizen and group interest articulation may be developing. Comparisons between Soviet and North American urban decision-making models are useful and valid, although they require an improved methodology and much more Soviet data.

Reducing biases of decision-making processes in complex organizations

2017 ◽  
Vol 40 (3) ◽  
pp. 270-291 ◽  
Author(s):  
Matteo Cristofaro
Keyword(s):  
Decision Making ◽  
Quality Control ◽  
Research Question ◽  
Decision Processes ◽  
Decision Makers ◽  
Problem Structuring Methods ◽  
Complex Organizations ◽  
Content Type ◽  
Quality Control Tool ◽  
Decision Making Processes

Purpose This paper aims to study how biases in decision-making processes could be reduced. In this vein, over the past 30 years, scholars interested in decision-making have been raising their interest in the development of quality control tools to mitigate the effects of cognitive distortions. However, they have often neglected the use of psychological instruments for understanding the role of decision-makers’ personality in the quality of the decision-making processes. Design/methodology/approach This is an intrinsic case study about an Italian complex organization (i.e. Consorzio ELIS) which tries to shed light on the identified research question. Three decision-makers responsible for the decision processes of three new business initiatives were interviewed using a recent quality control tool (i.e. checklist) and their personality types were tracked by performing MBTI® tests. The thematic analysis, approached by using NVivo software, and after six months of direct observations inside the organization, allowed an understanding of the decision processes and their distortions. Findings The results of this study show how initiatives with frequent quality control mechanisms and different stakeholders are more able to pass the decision phase than initiatives with no controls, few participants and little difference between personalities. Originality/value The results of this work show how reducing biases of decision-making processes in complex organizations can benefit from the simultaneous use of the checklist and MBTI® test. As demonstrated, when used together, they can make more effective use of and provide better results for both, as well as providing a better quality control of the decision-making processes. From that, an approach is proposed that both takes into account the two perspectives and can work together with other cognitive problem structuring methods.

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