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DSS research: a bibliometric analysis by gender DSS研究:按性别分类的文献计量分析
IF 3.4 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2022-05-03 DOI: 10.1080/12460125.2022.2070953
P. Keenan, C. Heavin
ABSTRACT This research-in-progress article uses a bibliometric approach to explore the research landscape by the gender of publishing authors in the Decision Support Systems (DSS) field over 10 years, from 2011 to 2020. The Web of Science (WOS) provides a valuable information resource on academic disciplines as it contains both the articles published and the articles cited. This research presents information on the gender breakdown of authors publishing on the topic of DSS globally. We examined publication trends over time, considering the main categories and research areas by authors’ gender. As a result, some initial recommendations to guide future research efforts of both DSS academics and practitioners are provided.
摘要:这篇正在进行的研究文章采用文献计量方法,从2011年到2020年,在10年的时间里,按出版作者的性别探索决策支持系统(DSS)领域的研究前景。科学网(WOS)提供了一个关于学术学科的宝贵信息资源,因为它包含了发表的文章和引用的文章。这项研究提供了全球DSS主题出版作者的性别分类信息。我们研究了一段时间以来的出版趋势,根据作者的性别考虑了主要类别和研究领域。因此,提供了一些初步建议,以指导DSS学者和从业者未来的研究工作。
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引用次数: 0
Data-driven decision making: new opportunities for DSS in data stream contexts 数据驱动决策:数据流环境下DSS的新机遇
IF 3.4 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2022-05-02 DOI: 10.1080/12460125.2022.2071404
Nuria Mollá, C. Heavin, A. Rabasa
ABSTRACT Traditionally, Decision Support Systems (DSS) data were stored statically and persistently in a database. Increasing volume and intensity of information and data streams create new opportunities and challenges for DSS experts, data scientists, and decision makers. Novel data stream contexts require that we move beyond static DSS modelling techniques to support data-driven decision-making. Implementing incremental and/or adaptive algorithms may help to solve some of the challenges arising from data streams. This research investigates the use of these algorithms to better understand how their performance compares with more traditional approaches. We show that an adaptive DSS engine has the potential to identify errors and improve the accuracy of the model. We briefly identify how this approach could be applied to unexpected highly uncertain decision scenarios. Future research considers new opportunities to pursue a multidisciplinary approach to adaptive DSS design, development, and implementation leveraging emerging machine learning techniques in tackling complex decision problems.
摘要传统上,决策支持系统(DSS)数据是静态持久地存储在数据库中的。信息和数据流的数量和强度不断增加,为DSS专家、数据科学家和决策者带来了新的机遇和挑战。新的数据流上下文要求我们超越静态DSS建模技术,支持数据驱动的决策。实现增量和/或自适应算法可能有助于解决数据流带来的一些挑战。这项研究调查了这些算法的使用,以更好地了解它们与更传统的方法相比的性能。我们证明了自适应DSS引擎有可能识别错误并提高模型的准确性。我们简要介绍了如何将这种方法应用于出乎意料的高度不确定的决策场景。未来的研究考虑了新的机会,利用新兴的机器学习技术来解决复杂的决策问题,寻求多学科的自适应DSS设计、开发和实施方法。
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引用次数: 3
Support for cognition in decision support systems: an exploratory historical review 决策支持系统对认知的支持:一个探索性的历史回顾
IF 3.4 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2022-04-29 DOI: 10.1080/12460125.2022.2070946
G. Phillips-Wren, Mary Daly, F. Burstein
ABSTRACT Decision support systems (DSS) have been traditionally developed to assist with unstructured and semi-structured problems. Early DSS researchers explored a broad range of techniques for supporting human cognition as part of decision making. Cognition during decision making was viewed in terms of two competing, and sometimes cooperating, systems: one that was automatic and fast, and one that was deliberative and slow. The aim of this research is to trace historical studies on cognitive aspects of decision support and determine the theoretical underpinnings of DSS support for cognition. We analysed articles drawing on the seminal literature to derive the relevant dimensions, including the classical Gorry & Scott Morton (1989) framework. This analysis identified opportunities for future research relevant to providing better support for cognition by highlighting some design parameters for information systems.
决策支持系统(DSS)传统上是为解决非结构化和半结构化问题而开发的。早期DSS研究人员探索了一系列支持人类认知的技术,将其作为决策的一部分。决策过程中的认知被视为两个相互竞争、有时相互合作的系统:一个是自动的快速系统,另一个是深思熟虑的缓慢系统。本研究的目的是追溯决策支持的认知方面的历史研究,并确定决策支持对认知的理论基础。我们分析了借鉴开创性文献的文章,以得出相关的维度,包括经典的Gorry&Scott Morton(1989)框架。该分析通过强调信息系统的一些设计参数,为未来的研究提供了机会,为认知提供更好的支持。
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引用次数: 2
Distance-based aggregation in group AHP 群AHP中基于距离的聚合
IF 3.4 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2022-04-29 DOI: 10.1080/12460125.2022.2070952
Zsombor Szádoczki, S. Duleba
ABSTRACT The aggregation of evaluators’ preferences is a key problem in group decision making. We examine the recently proposed distance-based techniques and compare their efficiency to the traditional aggregation of individual preferences (AIP) methods in simulated Analytic Hierarchy Process (AHP) cases. We use the Kendall W statistic to measure the rank correlation among the individual priority vectors of the group and the common priority vector for the different aggregation approaches. Extensive simulations (altogether 88000 cases) show that both the Euclidean Distance-Based Aggregation Method (EDBAM) and the Aitchison Distance-Based Aggregation Method significantly outperform the traditional techniques in case of smaller and mid-sized priority vectors (at most six items to be compared). However, EDBAM outperform the AIP methods for all dimensions that is conventionally used in AHP, and its computation time is also low.
评价者偏好的聚合是群体决策中的一个关键问题。我们研究了最近提出的基于距离的技术,并在模拟层次分析过程(AHP)的情况下将其与传统的个人偏好聚合(AIP)方法的效率进行了比较。我们使用Kendall W统计量来衡量组的单个优先向量和不同聚合方法的公共优先向量之间的等级相关性。广泛的模拟(总共88000个案例)表明,在较小和中等大小的优先向量(最多六个项目进行比较)的情况下,欧几里得基于距离的聚合方法(EDBAM)和艾奇逊基于距离的聚合方法都明显优于传统技术。然而,EDBAM在AHP中通常使用的所有维度上都优于AIP方法,而且它的计算时间也很低。
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引用次数: 1
AI ethical biases: normative and information systems development conceptual framework 人工智能伦理偏见:规范和信息系统发展概念框架
IF 3.4 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2022-04-20 DOI: 10.1080/12460125.2022.2062849
T. Chowdhury, J. Oredo
ABSTRACT Alongside the revolutionary benefits of AI, it can cause numerous problems across the system development process. AI ecosytem players have recently started to interrogate the ethical biases implicit in AI-enabled applications and agents. The contestable nature of ethics and the complexity of AI-enabled applications has led to incoherent literature around AI ethical biases. The numerous conceptions of AI ethics and a multiplicity of ethical biases has compounded matters for researchers, practitioners, and policy makers. The current study proposes a conceptual framework to organize AI ethical biases. A narrative literature review was conducted to identify and group the biases into data biases, method biases and implementation biases. The CRISP-DM framework was used to classify the ethical biases. The emerging conceptual framework has four clusters that represents: System development phases, scope of ethical bias, exemplars, and possible solutions. The study extends the existing AI ethical frameworks and provides a unified communication artefact for practitioners.
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引用次数: 2
The role of management in fostering analytics: the shift from intuition to analytics-based decision-making 管理在培养分析中的作用:从直觉到基于分析的决策的转变
IF 3.4 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2022-04-12 DOI: 10.1080/12460125.2022.2062848
Philipp Korherr, D. Kanbach, S. Kraus, Paul Jones
ABSTRACT Research shows that many firms still make business critical decisions intuitively, despite clear evidence that analytics-based decision-making is likely more effective in creating corporate and social value. With the aim of providing actionable guidance to firms on how to accomplish the shift to analytics-based decision-making, this paper sheds light on the management factors that prove critical in this context. An in-depth single-site case study was conducted with a large publicly listed German manufacturing company. Building on 22 semi-structured interviews, this empirical study identifies six factors that play a critical role in establishing analytics-based decision-making: management behaviour, top management and strategy, analytics infrastructure, organisation and governance, HR management and development, and culture. This study forms the basis for further scientific research on the role of firm management in the transitional phase. Furthermore, it provides firm leaders with a systemised and practical framework to structure firm efforts to establish data-based decision making.
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引用次数: 13
Role of Executive Sponsors in business analytics success – Understanding their influence domains using Deductive Thematic Analysis 执行发起人在商业分析成功中的作用-使用演绎主题分析了解他们的影响领域
IF 3.4 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2022-04-08 DOI: 10.1080/12460125.2022.2043576
S. Srinivasan, Amit Agrahari, Ashwani Kumar
ABSTRACT Analytics success has delivered superior customer value, higher revenue growth, and enhanced profitability to organizations. However, a large percentage of organizations in each industry has failed to realize such analytics success. Past studies have identified the antecedents of success and failure of business analytics projects. Executive Sponsorship was rated as among the top-success enablers. There has been limited attention to how executive sponsors influence business analytics success. To demystify Executive Sponsor’s role, we applied Deductive Thematic Analysis to the narratives recorded from key informants for 21 successful and 20 failed analytics projects covering 14 industries. This study identified four influence domains that span the project lifecycle and recommends the Executive Sponsor’s involvement in each of these. The influence domains are Project Initiation, Organization culture, User trust, and Deployment enablers. Our informants found the influence domains comprehensive to explain the outcomes of their projects. The influence domains can help articulate the role of the Executive Sponsor better and set expectations. They can also be used to analyze projects, identify causes of failure, and plan corrective actions.
摘要分析的成功为组织带来了卓越的客户价值、更高的收入增长和更高的盈利能力。然而,每个行业中都有很大一部分组织未能实现这样的分析成功。过去的研究已经确定了商业分析项目成功和失败的前因。高管赞助被评为最成功的推动者之一。对高管赞助商如何影响商业分析成功的关注有限。为了揭开执行发起人角色的神秘面纱,我们将演绎主题分析应用于关键线人记录的21个成功和20个失败分析项目的叙述,这些项目涵盖了14个行业。本研究确定了跨越项目生命周期的四个影响领域,并建议执行发起人参与其中的每一个领域。影响领域包括项目发起、组织文化、用户信任和部署推动者。我们的线人发现影响领域很全面,可以解释他们项目的结果。影响力领域可以帮助更好地阐明执行发起人的角色,并设定期望值。它们还可以用于分析项目、确定失败原因和计划纠正措施。
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引用次数: 0
Value creation from analytics with limited data: a case study on the retailing of durable consumer goods 从有限数据的分析中创造价值:以耐用消费品零售为例
IF 3.4 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2022-04-07 DOI: 10.1080/12460125.2022.2059172
K. Hopf, Andreas Weigert, T. Staake
ABSTRACT Companies are pinning high hopes on competitive advantages through data analytics. So far, value gains through analytics have been demonstrated for IT-heavy and data-rich business areas. Yet, research has paid little attention to value creation through data analytics in the plethora of companies with limited data (i.e. having transactions in the hundreds and attributes in the tens). Building on the literature of big data value creation and the resource-based view, we carried out an in-depth analytics case study with a retailer of renewable energy systems. Firms in this business area operate with expensive but few sales, so their available data are notoriously limited. Our findings demonstrate that data analytics capabilities and value creation mechanisms (democratise, contextualise, experiment with data, and execute data insights) are also effective in situations with limited data. Practice and research should therefore put not only emphasis on the volume and the variety of data but also on contextual factors related to managers (e.g. clear strategy, vision, leadership) and all employees (e.g. openness for agile working mode, data awareness).
摘要企业对通过数据分析获得的竞争优势寄予厚望。到目前为止,通过分析获得的价值已经在IT密集和数据丰富的业务领域得到了证明。然而,研究很少关注通过数据分析在大量数据有限的公司中创造价值(即交易数量为数百,属性数量为数十)。基于大数据价值创造的文献和基于资源的观点,我们对一家可再生能源系统零售商进行了深入的分析案例研究。这一业务领域的公司经营成本高昂,但销售额很少,因此他们的可用数据是出了名的有限。我们的研究结果表明,数据分析能力和价值创造机制(民主化、情境化、数据实验和执行数据洞察)在数据有限的情况下也是有效的。因此,实践和研究不仅应重视数据的数量和多样性,还应重视与管理者(如明确的战略、愿景、领导力)和所有员工相关的背景因素(如敏捷工作模式的开放性、数据意识)。
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引用次数: 7
Success factors for managing the SSBI challenges of the AQUIRE framework 管理AQUIRE框架的SSBI挑战的成功因素
IF 3.4 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2022-03-23 DOI: 10.1080/12460125.2022.2057006
Christian Lennerholt, J. V. Laere, Eva Söderström
ABSTRACT Self-service business intelligence (SSBI) enables all users, including those with limited technical skills, to perform business intelligence (BI) tasks without the support of BI experts. SSBI reduces pressure on BI experts, gives more freedom to self-reliant users and speeds up decision-making. Recent research has illustrated how organisations experience numerous challenges when trying to obtain SSBI benefits. The AQUIRE framework organises 37 identified SSBI challenges in five categories: A ccess and use of data, Data Q uality, U ser I ndependence, creating R eports and E ducation. SSBI literature does poorly address how these challenges can be tackled. This research study aimed to identify strategies on how to manage those 37 SSBI challenges. The performed case study includes 24 semi-structured interviews with respondents from two organisations which have been heavily involved in SSBI implementation. The results reveal how nine identified SSBI success factors are related to the 37 AQUIRE challenges and how they can be addressed over time.
摘要自助式商业智能(SSBI)使所有用户(包括技术技能有限的用户)能够在没有BI专家支持的情况下执行商业智能(BI)任务。SSBI减轻了BI专家的压力,为自力更生的用户提供了更多的自由,并加快了决策速度。最近的研究表明,组织在试图获得SSBI利益时会遇到许多挑战。AQUIRE框架将37个已确定的SSBI挑战分为五类:数据的访问和使用、数据质量、用户独立性、创建报告和教育。SSBI文献并没有很好地说明如何应对这些挑战。本研究旨在确定如何应对这37项SSBI挑战的策略。所进行的案例研究包括对两个组织的受访者进行的24次半结构化访谈,这两个组织都参与了SSBI的实施。研究结果揭示了9个已确定的SSBI成功因素与37个AQUIRE挑战之间的关系,以及如何随着时间的推移加以解决。
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引用次数: 5
Affected but not involved: Two-scenario based investigation of individuals’ attitude towards decision support systems based on the example of the video assistant referee 受影响但未参与:以视频助理裁判为例,对个人对决策支持系统的态度进行两种情景调查
IF 3.4 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Pub Date : 2022-02-27 DOI: 10.1080/12460125.2022.2041274
Julian Märtins, D. Westmattelmann, G. Schewe
ABSTRACT To fully realize the benefits of Decision Support Systems (DSS), it is important to investigate factors influencing individuals who are affected by the DSS’ decision but are not involved in decision-making. An example of such DSS is the Video Assistant Referee (VAR) in professional football. Drawing on transparency and justice research, we examined the role of transparency, procedural justice, and social influence on individuals’ attitudes towards the VAR. A quantitative vignette-based approach (N = 824) using two scenarios (fans watching from home/in stadiums) was chosen. Results indicate that all variables are higher in the home setting. Structural equation modelling revealed that transparency, procedural justice, and social influence significantly impact individual’s attitude towards the VAR. Multigroup analyses showed that the effect size of one transparency dimension is significantly stronger at home, while social influence is stronger in stadiums. To further interpret the findings, we conducted twelve semi-structured interviews among football fans.
摘要为了充分发挥决策支持系统的优势,研究受决策支持系统决策影响但未参与决策的个体的影响因素是很重要的。这种DSS的一个例子是职业足球中的视频助理裁判(VAR)。在透明度和公正性研究的基础上,我们研究了透明度、程序公正性和社会影响对个人对VAR态度的影响。选择了一种基于量化小插曲的方法(N=824),使用两种场景(球迷在家/体育场观看)。结果表明,在家庭环境中,所有变量都较高。结构方程模型显示,透明度、程序公正性和社会影响显著影响个人对VAR的态度。多组分析表明,一个透明度维度的影响大小在国内显著更强,而社会影响在体育场馆更强。为了进一步解释研究结果,我们对足球迷进行了12次半结构化访谈。
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引用次数: 4
期刊
Journal of Decision Systems
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