A multidisciplinary perspective of big data in management research

Sheng, Jie; Amankwah‐Amoah, Joseph; Wang, Xiaojun

doi:10.1016/j.ijpe.2017.06.006

Cited by 178 publications

(172 citation statements)

References 287 publications

(178 reference statements)

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“…Consistent with this perspective, some have suggested that this era of big data requires organizations to become more agile and proactive in identifying threats and opportunities and take advantage of any found opportunities using sources such as customers’ reviews and online blogs to innovate and improve competitiveness (e.g. George, Haas and Pentland, ; Sheng, Amankwah‐Amoah and Wang, ; Teece, Peteraf and Leih, ). After sensing a change in consumers’ behaviour or new opportunities stemming from online review data analysis (Sheng, Amankwah‐Amoah and Wang, , ), firms may not only have to redesign product and service offerings, but also change their business model (see Lavie, ; Teece, ).…”

Section: Overview Of Relevant Literaturementioning

confidence: 99%

“…George, Haas and Pentland, ; Sheng, Amankwah‐Amoah and Wang, ; Teece, Peteraf and Leih, ). After sensing a change in consumers’ behaviour or new opportunities stemming from online review data analysis (Sheng, Amankwah‐Amoah and Wang, , ), firms may not only have to redesign product and service offerings, but also change their business model (see Lavie, ; Teece, ). By harnessing user‐generated data, firms can innovate and respond to changing customers’ demands and environmental changes in a timely manner (Wessel, ).…”

Section: Overview Of Relevant Literaturementioning

confidence: 99%

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Managerial Responses to Online Reviews: A Text Analytics Approach

Sheng

Amankwah‐Amoah

Wang³

et al. 2019

British J of Management

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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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Section: Overview Of Relevant Literaturementioning

confidence: 99%

Section: Overview Of Relevant Literaturementioning

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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“…Technology diffusion encompasses technology adoption and technology use to improve the living conditions of individuals and the wider society (Lanzolla & Suarez, ). Some scholars have asserted that an effective mechanism for technology diffusion is information diffusion using communication channels such as social media platforms (see Sheng, Amankwah‐Amoah, & Wang, ) and traditional channels such as radio and television advertising to spread the potential gains/advantages associated with the adoption and use of latest technology (Lanzolla & Suarez, ). Some studies have indicated that effective communication channels are essential in spreading information about new technology as well as linking prior users and potential users of the technology (Attewell, ).…”

Section: Technological Diffusion and Scaling‐upmentioning

confidence: 99%

Technological revolution, sustainability, and development in Africa: Overview, emerging issues, and challenges

Amankwah‐Amoah

2019

Sustainable Development

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The paper examines the silent technological revolution in sub‐Saharan Africa focusing on emerging issues and challenges. In view of the centrality of technology diffusion in fostering local innovations and economic development in developing countries, it is surprising that our understanding of the challenges and opportunities in scaling‐up technologies remains limited. This paper capitalises on the ongoing silent technological revolution in sub‐Saharan Africa to present an overview of how new technologies have been adopted and utilised to achieve sustainability. The study identified a host of factors such as weak regulatory enforcement systems, lack of financial credit availability, and limited banking services, which have created conditions for technological innovations such as mobile phone‐based banking, mPedigree, “cardiopad,” and M‐PEPEA to emerge. The public policy implications and directions for future research are identified and examined.

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“…Nowadays, the Big Data is widely researched and used in both academics and industries on management [2] . In paper [3], factors influencing decision-making in management based on Big Data are identified and analyzed using a case study.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Big Data Model for Quality Tracing of Steel Industries

Yang¹,

Wang²,

Gao³

et al. 2018

dtcse

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Abstract. Quality tracing is a very important issue for steel industries. Due to the complexity and variety of quality data which tends to be changed into many forms during the whole product life cycle, it is difficult to express, record and trace through the whole product life cycle from designing, manufacturing, distributing, using and disposing. In this paper, according to the Big Data theory and the analysis of the characteristics of the steel products and the feature of their manufacturing processes, a dynamic integrated Big Data model of quality for steel industries is proposed for quality tracing issues. It is a dynamically growing multi-layered structure which consists of a four phases converting procedure: 1) the basis quality bill of material(BQBOM) which represents voice of customers; 2) the general process bill of material(GPBOM) which represents the process procedures, methods, and relative parameters during the generation of the products; 3) the production and scheduling bill of material(PSBOM) which represents the production plan controls and relative quality control specifications; and 4) the final quality bill of material(FQBOM) which represents the final product quality features to be delivered to customers and users. Techniques and procedures of the realization framework is also specified in this paper. This model would be useful for developers to build a quality system in a Big Data production environment.

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A multidisciplinary perspective of big data in management research

Cited by 178 publications

References 287 publications

Managerial Responses to Online Reviews: A Text Analytics Approach

Managerial Responses to Online Reviews: A Text Analytics Approach

Technological revolution, sustainability, and development in Africa: Overview, emerging issues, and challenges

A Dynamic Big Data Model for Quality Tracing of Steel Industries

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