Big data text analytics: an enabler of knowledge management

Khan, Zaheer; Vorley, Tim

doi:10.1108/jkm-06-2015-0238

Cited by 160 publications

(156 citation statements)

References 78 publications

(201 reference statements)

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“…The information systems literature broadly conceptualizes BDPA as organizational capability (Srinivasan and Swink, ), to process large volumes and varieties of data with the velocity required to gain relevant insights, thereby enabling organizations to gain competitive advantage (Akter et al ., ; Fosso Wamba et al ., ; Gupta and George, ; Pauleen and Wang, ; Srinivasan and Swink, ). The term ‘big data’ is often used to describe massive, complex and real‐time data that requires sophisticated management, analytical and processing techniques to extract management insights (Gupta and George, ; Jin et al ., ; Khan and Vorley, ; Sumbal, Tsui and See‐To, ; Wang and Zhang, ; Wang et al ., ). The predictive analytics statistical models try to predict future behaviour based on the assumption that what has happened in the past will continue to happen in the future.…”

Section: Introductionmentioning

confidence: 99%

Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture

Dubey

Gunasekaran

Childe

et al. 2019

British J of Management

545

395

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The importance of big data and predictive analytics has been at the forefront of research for operations and manufacturing management. The literature has reported the influence of big data and predictive analytics for improved supply chain and operational performance, but there has been a paucity of literature regarding the role of external institutional pressures on the resources of the organization to build big data capability. To address this gap, this paper draws on the resource‐based view of the firm, institutional theory and organizational culture to develop and test a model that describes the importance of resources for building capabilities, skills and big data culture and subsequently improving cost and operational performance. We test our research hypotheses using 195 surveys, gathered using a pre‐tested questionnaire. Our contribution lies in providing insights regarding the role of external pressures on the selection of resources under the moderating effect of big data culture and their utilization for capability building, and how this capability affects cost and operational performance.

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Section: Introductionmentioning

confidence: 99%

Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture

Dubey

Gunasekaran

Childe

et al. 2019

British J of Management

545

395

View full text Add to dashboard Cite

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“…Liu, Schuckert and Law, ; Tseng and Wu, ; Xiang et al ., ). The advent of the present data‐rich environment requires firms to mobilize and harness both structured and unstructured data sources to generate new knowledge and thus optimize operations (Khan and Vorely, ; Tian, ). For instance, through a data‐mining approach, Guo, Barnes and Jia () have extracted key dimensions/factors that influence consumer satisfaction from online hotel reviews and identified differences according to demographic segments.…”

Section: Overview Of Relevant Literaturementioning

confidence: 99%

“…Nevertheless, our present understanding of how firms can best capture and respond to the vast amount of available data remains relatively underdeveloped. Recent research suggests that technologies and social media text analytics can help firms harness the significant potential of both structured and unstructured data (Chen, Chiang and Storey, ; Khan and Vorely, ).…”

Section: Introductionmentioning

confidence: 99%

Managerial Responses to Online Reviews: A Text Analytics Approach

Sheng

Amankwah‐Amoah

Wang³

et al. 2019

British J of Management

Self Cite

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This study tests the effects of online managerial responses and returning customers’ future satisfaction (measured as review ratings) by performing social media text analytics on a hotel sample. Essentially, this paper provides insight into meaningful differences in future ratings between responding and non‐responding hotels, as well as differences in response styles between ratings improvement and non‐improvement. The results indicate that: (1) subsequent ratings are higher if customers receive responses to their previous online reviews; (2) increase in ratings is more significant among low‐satisfaction customers, and a decrease in ratings is mitigated if responses are provided; (3) responding to loyal customers – those who have visited and rated the same hotel more than three times – has a limited impact on ratings; (4) responses are longer and sentiment is slightly lower in scenarios where subsequent ratings are improved, but there is no significant difference in the effect of response speed between the two groups; (5) changes in ratings also affect styles of responding to current reviews – if customer satisfaction has improved, response length tends to be shorter and sentiment level tends to be higher. The findings offer both theoretical and managerial implications by demonstrating the utility of social media text analytics.

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“…Companies with higher levels of EO can be in a better position to effectively utilize big data-related knowledge assets for both the discovery and exploitation of opportunities arising from big data analytics (Covin and Lumpkin, 2011;Davenport and Patil, 2012;Khan and Vorley, 2017).…”

Section: Entrepreneurial Orientation and Value Creation From Big Datamentioning

confidence: 99%

Value creation through big data in emerging economies

Zeng

Khan

2019

Self Cite

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Purpose -The purpose of this paper is to examine how managers orchestrate, bundle, and leverage resources from big data for value creation in emerging economies.Design/methodology/approach -The authors grounded the theoretical framework in two perspectives: the resource management and entrepreneurial orientation. The study utilizes an inductive, multiple-case research design to understand the process of creating value from big data.Findings -The findings suggest that entrepreneurial orientation is vital through which companies based in emerging economies can create value through big data by bundling and orchestrating resources thus improving performance.Originality/value -This is one of the first studies to have integrated resource orchestration theory and entrepreneurial orientation in the context of big data and explicate the utility of such theoretical integration in understanding the value creation strategies through big data in the context of emerging economies.

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Big data text analytics: an enabler of knowledge management

Cited by 160 publications

References 78 publications

Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture

Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture

Managerial Responses to Online Reviews: A Text Analytics Approach

Value creation through big data in emerging economies

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