How do top- and bottom-performing companies differ in using business analytics?

Duan, Yanqing

doi:10.1108/jeim-04-2016-0080

Cited by 25 publications

(24 citation statements)

References 58 publications

(120 reference statements)

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“…In general, results show that the assumed relationship between big data and organizational performance might exist (e.g. Cao & Duan, 2017;Gunasekaran et al, 2017). Also the link between big data and decision making is supported in different studies as shown in Figure 1 (e.g.…”

Section: The Relationship Of Big Data and Organizational Performance mentioning

confidence: 71%

From Big Data to Big Performance – Exploring the Potential of Big Data for Enhancing Public Organizations’ Performance. A Systematic Literature Review

Guirguis

2020

Yearbook of Swiss Administrative Sciences

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This article examines the possibilities for increasing organizational performance in the public sector using big data by conducting a systematic literature review. It includes the results of 36 scientific articles published between January 2012 and July 2019. The results show a tendency to explain the relationship between big data and organizational performance through the Resource-Based View of the Firm or the Dynamic Capabilities View, arguing that performance improvement in an organization stems from unique capabilities. In addition, the results show that big data performance improvement is influenced by better organizational decision making. Finally, it identifies three dimensions that seem to play a role in this process: the human dimension, the organizational dimension, and the data dimension. From these findings, implications for both practice and theory are derived.

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Section: The Relationship Of Big Data and Organizational Performance mentioning

confidence: 71%

From Big Data to Big Performance – Exploring the Potential of Big Data for Enhancing Public Organizations’ Performance. A Systematic Literature Review

Guirguis

2020

Yearbook of Swiss Administrative Sciences

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“…Similarly, Chen and Nath (2018) suggest that the integration of business analytics into business processes can lead to improvements in organizational performance. Furthermore, Cao and Duan (2017) conclude that greater affinity between the BI and organizational processes can increase the efficiency of data-driven decision-making, thereby positively influencing organizational performance. Hence, it is likely that the alignment of BI and BPM positively affects organizational performance.…”

Section: The Alignment Of Business Intelligence and Business Process mentioning

confidence: 94%

Business intelligence and organizational performance

Glavan

Vukšić

Bach

et al. 2020

BPMJ

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PurposeOrganizations introduce business intelligence (BI) to increase their performance, but often, this initiative is not aligned with the business process management (BPM) initiative, which also aims to improve organizational performance. Although some findings from the literature indicate that BI implementation has a positive impact on organizational performance, the impact seems to be indirect. Therefore, the purpose of this study is to enhance the understanding of how BI maturity is translated into organizational performance. Alignment of BI and BPM initiatives seems one possible way for creating business value with BI, particularly because BI enables process performance measurement and management, which allows the BI initiative to become more business focused.Design/methodology/approachA questionnaire was prepared and used to collect data in Croatian and Slovenian organizations with more than 50 employees. A BI–BPM alignment measurement instrument was developed for the purpose of this study using the recommended process of scale development and validation. A total of 185 responses were analyzed by the structural equation modeling technique.FindingsOur results provide evidence that the effect of BI on organizational performance is fully mediated by alignment of BI and BPM initiatives, and therefore, BI business value can be generated through the use of common terminology and methodologies, as well as a strong communication between BI and BPM experts, managers and teams in order to coordinate the two initiatives.Originality/valueThis study has responded to the call for better understanding of how the impact of BI on organization performance is realized. It confirmed that BI and BPM initiatives should be aligned in order to give BI a business value.

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“…Authors have also identified internal factors including company size (Cao et al, 2015;Davenport, Harris & Morison, 2010), the data-savviness of employees and a data-driven culture (Acito & Khatri, 2014;Carillo, 2017;Gupta & George, 2016;Ransbotham, Kiron & Prentice, 2016), executive support, prior successes, and available expertise (Acito & Khatri, 2014;Ransbotham et al, 2016). Another moderating factor is the fit of Analytics to organizational strategy, structure, and processes (Cao & Duan, 2017). This underlines that one Analytics approach of an organization cannot simply be transferred to another.…”

Section: The Matter Of Domain In Analyticsmentioning

confidence: 99%

“…That way, analysts are more aware of the organization's main activities, challenges, and processes and can develop greater understanding of the organization overall including strategy and value creation potential from Analytics solutions (Harris et al, 2010). Thereby, the fit to strategy is indicated as a distinguishing factor between low and high performers (Cao & Duan, 2017). Rotation further creates exchange between domains and stimulates the adoption of Analytics across domains (Lavalle et al, 2011).…”

Section: Modes Of Incorporating Domain Knowledge In Analytics Initiatmentioning

confidence: 99%

Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

Herden

2020

JIEM

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Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains.Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin.Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed.Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.

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How do top- and bottom-performing companies differ in using business analytics?

Cited by 25 publications

References 58 publications

From Big Data to Big Performance – Exploring the Potential of Big Data for Enhancing Public Organizations’ Performance. A Systematic Literature Review

From Big Data to Big Performance – Exploring the Potential of Big Data for Enhancing Public Organizations’ Performance. A Systematic Literature Review

Business intelligence and organizational performance

Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

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