scholarly journals Research-Based Guidelines for Marketing Information Systems

2021 ◽  
Vol 2 (1) ◽  
pp. 1-16
Author(s):  
Albérico Travassos Rosário
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Literature Review ◽  
Best Practices ◽  
Empirical Studies ◽  
Internet Marketing ◽  
Review Of The Literature ◽  
Marketing Intelligence ◽  
Marketing Information

Marketing information systems (MKIS) are decision support systems focused on specific marketing decisions, providing a more efficient and effective framework for analyzing and identifying changes in the market environment. The literature review reveals that there are gaps in theoretical and empirical studies about which specific steps and best practices should be considered in MKIS implementation efforts. Is it intended to synthesize the knowledge and perceptions generated by existing MKIS studies and identify the generic and particular guidelines that can be derived from the existing body of MKIS research. A review of the literature leads the authors to a thematic synthesis that generates five core guidelines for MKIS: (1) developing, implementing, and measuring the effectiveness of the MKIS; (2) how to align the MKIS with organizational strategy and decision-making; (3) leveraging the MKIS resources in internet marketing; (4) generating and applying marketing intelligence; and, finally, (5) leveraging the benefits of the MKIS in terms of accumulating knowledge and marketing intelligence.

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

scholarly journals The Effect of Perceived Factors of Decision Support Systems on Actual Usage: Behavioral Intention of Using Decision Support Systems as a Mediating Variable: “An Empirical Study of Jordanian Higher Education Institutions”

2016 ◽  
Vol 12 (1) ◽  
pp. 201
Author(s):  
Bilal Mohammed Salem Al-Momani
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Behavioral Intention ◽  
Support Systems ◽  
Higher Education Institutions ◽  
Quality Of Information

Decision support systems (DSS) are interactive computer-based systems that provide information, modeling, and manipulation of data. DSS are clearly knowledge-based information systems to capture, Processing and analysis of information affecting or aims to influence the decision making process, performed by people in scope professional job appointed by a user. Hence, this study describes briefly the key concepts of decision support systems such as perceived factors with a focus on quality  of information systems and quality of information variables, behavioral intention of using DSS, and actual DSS use by adopting and extending the technology acceptance model (TAM) of Davis (1989); and Davis, Bagozzi and Warshaw (1989).There are two main goals, which stimulate the study. The first goal is to combine Perceived DSS factors and behavioral intention to use DSS from both the social perspective and a technology perspective with regard to actual DSS usage, and an experimental test of relations provide strategic locations to organizations and providing indicators that should help them manage their DSS effectiveness. Managers face the dilemma in choosing and focusing on most important factors which contributing to the positive behavioral intention of use DSS by the decision makers, which, in turn, could contribute positively in the actual DSS usage by them and other users to effectively solve organizational problems. Hence, this study presents a model which should provide the useful tool for top management in the higher education institutions- in particular-to understand the factors that determine using behaviors for designing proactive interventions and to motivate the acceptance of TAM in order to use the DSS in a way that contributes to the higher education decision-making plan and IT policy.To accomplish or attain the above mentioned objectives, the researcher developed a research instrument (questionnaire) and distributed it amongst the higher education institutions in Jordan to collect data in order to empirically study hypothesis testing (related to the objectives of study). 341 questionnaires were returned from the study respondents. Data were analyzed by utilizing both SPSS (conducted descriptive analysis) and AMOS (conducting structural equation modelling).Findings of the study indicate that some hypotheses were supported while the others were not. Contributions of the study were presented. In addition, the researcher presented some recommendations. Finally, this study has identified opportunities for further study which has progressed greatly advanced understanding constantly of DSS usage, that can help formulate powerful strategies Involving differentiation between DSS perceived factors.

A proactive decision making framework for condition-based maintenance

2015 ◽  
Vol 115 (7) ◽  
pp. 1225-1250 ◽  
Author(s):  
Alexandros Bousdekis ◽  
Babis Magoutas ◽  
Dimitris Apostolou ◽  
Gregoris Mentzas
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Real Time ◽  
Condition Based Maintenance ◽  
Sensor Data ◽  
Extensive Literature ◽  
Decision Space ◽  
Content Type ◽  
Extensive Literature Review

Purpose – The purpose of this paper is to perform an extensive literature review in the area of decision making for condition-based maintenance (CBM) and identify possibilities for proactive online recommendations by considering real-time sensor data. Based on these, the paper aims at proposing a framework for proactive decision making in the context of CBM. Design/methodology/approach – Starting with the manufacturing challenges and the main principles of maintenance, the paper reviews the main frameworks and concepts regarding CBM that have been proposed in the literature. Moreover, the terms of e-maintenance, proactivity and decision making are analysed and their potential relevance to CBM is identified. Then, an extensive literature review of methods and techniques for the various steps of CBM is provided, especially for prognosis and decision support. Based on these, limitations and gaps are identified and a framework for proactive decision making in the context of CBM is proposed. Findings – In the proposed framework for proactive decision making, the CBM concept is enriched in the sense that it is structured into two components: the information space and the decision space. Moreover, it is extended in a way that decision space is further analyzed according to the types of recommendations that can be provided. Moreover, possible inputs and outputs of each step are identified. Practical implications – The paper provides a framework for CBM representing the steps that need to be followed for proactive recommendations as well as the types of recommendations that can be given. The framework can be used by maintenance management of a company in order to conduct CBM by utilizing real-time sensor data depending on the type of decision required. Originality/value – The results of the work presented in this paper form the basis for the development and implementation of proactive Decision Support System (DSS) in the context of maintenance.

scholarly journals Accompanying the Prostate Cancer Patient Pathway: Evaluation of Novel Clinical Decision Support Software

2021 ◽  
Author(s):  
Maurice Henkel ◽  
Tobias Horn ◽  
Francois Leboutte ◽  
Pawel Trotsenko ◽  
Sarah G. Dugas ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Data Quality ◽  
Clinical Decision Support ◽  
User Satisfaction ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Decision Making Process

Abstract Introduction Physicians spend more than half of their workday interacting with health information systems to care for their patients. Effective data management that provides physicians with comprehensive patient information from various information systems is required to ensure high quality clinical decision making.Objectives We evaluated the impact of a novel, CE-certified clinical decision support tool on physician’s effectiveness and satisfaction in the clinical decision-making process.Methods Using pre-therapeutic prostate cancer management cases, we compared physician’s expenditure of time, data quality, and user satisfaction in the decision-making process comparing the current standard with the software. Ten urologists from our department conducted the diagnostic work-up to the treatment decision for a total of 10 patients using both approaches.Results A significant reduction in the physician’s expenditure of time for the decision-making process by -59.9 % (p < 0,001) was found using the software. System usage showed a high positive effect on evaluated data quality parameters completeness (Cohen's d of 2.36), format (6.15), understandability (2.64), as well as user satisfaction (4.94).Conclusion The software demonstrated that effective data management can improve physician’s effectiveness and satisfaction in the clinical decision-making process. Further development is needed to map more complex patient pathways, such as the follow-up treatment of prostate cancer.

scholarly journals Best practices in implementing a project management office: a systematic review of the literature

2020 ◽  
Vol 14 (4) ◽  
pp. 448-463
Author(s):  
Gustavo Oliveira Pinto ◽  
Luiz Carlos Brasil de Brito Mello ◽  
Thaís Spiegel
Keyword(s):  
Project Management ◽  
Literature Review ◽  
Best Practices ◽  
Systematic Literature Review ◽  
Management Practices ◽  
Review Of The Literature ◽  
Project Management Office ◽  
Strategic Objectives ◽  
Organizational Variables ◽  
Implementation Challenges

Highlights: Project management office (PMO) is structurally configured in a particular way to adapt to the peculiarities of each organization and its strategic objectives, in order to promote project management practices. The purpose of this article is to explore the best practices for project management office implementation. A systematic literature review was conducted using 104 documents published between 2000 and 2018. The research allowed the identification of PMO's data, such as functions, models, best practices in implementation, challenges to implementation, and success factors.Goal: The purpose of this article is to explore the best practices for project management office implementation.Design/Methodology/Approach: A systematic literature review was conducted using 104 documents published between 2000 and 2018.Results: The research allowed the identification of PMO's data, such as functions, models, best practices in implementation, challenges to implementation, and success factors.Limitations of the investigation: Other factors related to PMO, such as the implementation phases, maturity models, process groups, and organizational variables that affect PMO.Practical implications: It is observed that there are relevant issues in PMO implementation structuring that are not consolidated, making it difficult for organizations to base their implementation on the available theoretical frameworks.Originality/value: As a result, it became evident that there is a lack of standardization of those characteristics related to the PMO; and that the so-called "best practices" require more academic studies before they can be established.

scholarly journals Horizontal collaboration in the freight transport sector: barrier and decision-making frameworks

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Ahmed Karam ◽  
Kristian Hegner Reinau ◽  
Christian Richter Østergaard
Keyword(s):  
Decision Making ◽  
Sustainable Development ◽  
Decision Support ◽  
Literature Review ◽  
Freight Transport ◽  
Decision Makers ◽  
Transport Sector ◽  
Decision Making Process ◽  
Transport Networks ◽  
Collaborative Decision

AbstractIn the freight transport sector, competing companies horizontally collaborate through establishing Collaborative Transport Networks (CTNs). Fruitful implementation of CTNs will leverage environmental and socio-economic goals of sustainable development in the freight transport sector. The benefits of CTNs in horizontal collaborative settings have been widely demonstrated through several modelling approaches. However, in practice, the real applications of CTNs have been challenging and most did not achieve satisfactory performances. Some studies have addressed this issue by identifying different barriers to CTN implementation. However, a conceptual framework for the barriers is not well-established. In addition, the literature lacks a decision-making framework for the CTN implementation which considers the different barriers. To address this gap, this paper conducted a literature review of the barriers to CTN implementation. In total, 31 different barriers were identified. A conceptual barrier framework is developed by grouping the 31 barriers into five categories: the business model, information sharing, the human factors, the Collaborative Decision Support Systems (CDSSs), and the market. The paper additionally proposes a stage-gate model integrating the conceptual barrier framework into the CTN implementation decision-making process. The current work contributes to the existing literature by developing both theoretical and practical understandings of the barriers to implementing CTNs and will support decision makers in CTN implementation to maximize the CTN benefits and minimize the risk of CTN failure.

scholarly journals The relationship between marketing intelligence and strategic marketing

2014 ◽  
Vol 17 (4) ◽  
pp. 440-456 ◽  
Author(s):  
Peet Venter ◽  
Mari Jansen van Rensburg
Keyword(s):  
Decision Making ◽  
South African ◽  
Communication Technology ◽  
Decision Makers ◽  
Strategic Marketing ◽  
Marketing Decision ◽  
Information And Communication ◽  
Marketing Intelligence ◽  
Marketing Information ◽  
The Relationship

Despite the importance attached to MI and other marketing information functions, surprisingly few studies have explicitly examined the relationship between MI and strategic marketing decision-making. This article reports on a study conducted with the aim of determining the relationship between marketing intelligence (MI) and strategic marketing in South African organisations. A quantitative survey was conducted among 166 South African marketing decision-makers. The findings suggest a substantial gap between the importance and availability of key types of MI. Marketing decision-makers found the traditional MI and marketing tools of great value in supporting marketing decision-making, but the value of several of the newer MI tools and technologies was less clear. An analysis of MI practices suggested that MI quality and particularly information and communication technology (ICT) support for MI are areas requiring further attention. 

Closing the Gap between Data Mining and Business Users of Business Intelligence Systems

2012 ◽  
Vol 3 (4) ◽  
pp. 14-53 ◽  
Author(s):  
Ana Azevedo ◽  
Manuel Filipe Santos
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Easy Access ◽  
Complete Knowledge ◽  
Business Intelligence Systems ◽  
Closing The Gap

Since Lunh first used the term Business Intelligence (BI) in 1958, major transformations happened in the field of information systems and technologies, especially in the area of decision support systems. BI systems are widely used in organizations and their importance is recognized. These systems present themselves as essential parts of a complete knowledge of business and an irreplaceable tool in the support to decision making. The dissemination of data mining (DM) tools is increasing in the BI field, as well as the acknowledgment of the relevance of its usage in enterprise BI systems. BI tools are friendly, iterative, and interactive, allowing business users an easy access. The user can manipulate directly data, having the ability to extract all the value contained into that business data. Problems noted in the use of DM in the field of BI is related to the fact that DM models are complex in order to be directly manipulated by business users, not including BI tools. The nonexistence of BI tools allowing business users the direct manipulation of DM models was identified as the problem. More of these issues, possible solutions and conclusions are presented in this article.

Group Support Systems as Tools for HR Decision Making

2011 ◽  
pp. 1087-1095
Author(s):  
James Yao ◽  
John Wang
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Human Resource ◽  
Financial Management ◽  
Support Systems ◽  
Group Support Systems ◽  
Group Support ◽  
Soft Data

In the late 1960s, a new type of information system came about: model-oriented DSS or management decision systems. By the late 1970s, a number of researchers and companies had developed interactive information systems that used data and models to help managers analyze semistructured problems. These diverse systems were all called decision support systems (DSS). From those early days, it was recognized that DSS could be designed to support decision-makers at any level in an organization. DSS could support operations, financial management, and strategic decision making. Group decision support systems (GDSS) which aim at increasing some of the benefits of collaboration and reducing the inherent losses are interactive information technology-based environments that support concerted and coordinated group efforts toward completion of joint tasks (Dennis, George, Jessup, Nunamaker, & Vogel, 1998). The term group support systems (GSS) was coined at the start of the 1990s to replace the term GDSS. The reason for this is that the role of collaborative computing was expanded to more than just supporting decision making (Patrick & Garrick, 2006). For the avoidance of any ambiguities, the latter term shall be used in the discussion throughout this article. Human resources (HR) are rarely expected like other business functional areas to use synthesized data because HR groups have been primarily connected with transactional processing of getting data into the system and on record for reporting and historical purposes (Dudley, 2007). For them soft data do not win at the table; hard data do. Recently, many quantitative or qualitative techniques have been developed to support human resource management (HRM) activities, classified as management sciences/operations research, multiattribute utility theory, multicriteria decision making, ad hoc approaches, and human resource information systems (HRIS) (Byun, 2003). More importantly, HRIS can include the three systems of expert systems (ES), decision support systems (DSS), and executive information systems (EIS) in addition to transaction processing systems (TPS) and management information systems (MIS) which are conventionally accepted as an HRIS. As decision support systems, GSS are able to facilitate HR groups to gauge users’ opinions, readiness, satisfaction, and so forth, increase their HRM activity quality, and generate better group collaborations and decision makings with current or planned HRIS services. Consequently, GSS can help HR professionals exploit and make intelligent use of soft data and act tough in their decision-making process.

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