An experimental investigation of the impact of using big data analytics on customers’ performance measurement

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marwa Rabe Mohamed Elkmash ◽  
Magdy Gamal Abdel-Kader ◽  
Bassant Badr El Din
Keyword(s):  
Big Data ◽  
Performance Measurement ◽  
Data Analytics ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Future Research ◽  
Web Based ◽  
Content Type ◽  
The Impact

Purpose This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To accomplish the research aim, the theoretical discussion was developed through the combination of the diffusion of innovation theory with the technology acceptance model (TAM) that is less developed for the research field of this study. Design/methodology/approach Empirical data was obtained using Web-based quasi-experiments with 104 Egyptian accounting professionals. Further, the Wilcoxon signed-rank test and the chi-square goodness-of-fit test were used to analyze data. Findings The empirical results indicate that measuring customers’ performance based on BDA increase the organizations’ ability to analyze the customers’ unstructured data, decrease the cost of customers’ unstructured data analysis, increase the ability to handle the customers’ problems quickly, minimize the time spent to analyze the customers’ data and obtaining the customers’ performance reports and control managers’ bias when they measure customer satisfaction. The study findings supported the accounting professionals’ acceptance of BDA through the TAM elements: the intention to use (R), perceived usefulness (U) and the perceived ease of use (E). Research limitations/implications This study has several limitations that could be addressed in future research. First, this study focuses on customers’ performance measurement (CPM) only and ignores other performance measurements such as employees’ performance measurement and financial performance measurement. Future research can examine these areas. Second, this study conducts a Web-based experiment with Master of Business Administration students as a study’s participants, researchers could conduct a laboratory experiment and report if there are differences. Third, owing to the novelty of the topic, there was a lack of theoretical evidence in developing the study’s hypotheses. Practical implications This study succeeds to provide the much-needed empirical evidence for BDA positive impact in improving CPM efficiency through the proposed framework (i.e. CPM and BDA framework). Furthermore, this study contributes to the improvement of the performance measurement process, thus, the decision-making process with meaningful and proper insights through the capability of collecting and analyzing the customers’ unstructured data. On a practical level, the company could eventually use this study’s results and the new insights to make better decisions and develop its policies. Originality/value This study holds significance as it provides the much-needed empirical evidence for BDA positive impact in improving CPM efficiency. The study findings will contribute to the enhancement of the performance measurement process through the ability of gathering and analyzing the customers’ unstructured data.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Kumar Singh ◽  
Saurabh Agrawal ◽  
Abhishek Sahu ◽  
Yigit Kazancoglu
Keyword(s):  
Health Care ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Sector ◽  
Future Research ◽  
Research Gaps ◽  
Content Type

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

Big data analytics capabilities and knowledge management: impact on firm performance

2019 ◽  
Vol 57 (8) ◽  
pp. 1923-1936 ◽  
Author(s):  
Alberto Ferraris ◽  
Alberto Mazzoleni ◽  
Alain Devalle ◽  
Jerome Couturier
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Structural Equation ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Integrated Approach ◽  
Content Type ◽  
Decision Making Processes ◽  
External Sources

Purpose Big data analytics (BDA) guarantees that data may be analysed and categorised into useful information for businesses and transformed into big data related-knowledge and efficient decision-making processes, thereby improving performance. However, the management of the knowledge generated from the BDA as well as its integration and combination with firm knowledge have scarcely been investigated, despite an emergent need of a structured and integrated approach. The paper aims to discuss these issues. Design/methodology/approach Through an empirical analysis based on structural equation modelling with data collected from 88 Italian SMEs, the authors tested if BDA capabilities have a positive impact on firm performances, as well as the mediator effect of knowledge management (KM) on this relationship. Findings The findings of this paper show that firms that developed more BDA capabilities than others, both technological and managerial, increased their performances and that KM orientation plays a significant role in amplifying the effect of BDA capabilities. Originality/value BDA has the potential to change the way firms compete through better understanding, processing, and exploiting of huge amounts of data coming from different internal and external sources and processes. Some managerial and theoretical implications are proposed and discussed in light of the emergence of this new phenomenon.

The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility

2019 ◽  
Vol 32 (2) ◽  
pp. 297-318 ◽  
Author(s):  
Santanu Mandal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Investment Decision ◽  
Big Data Analytics ◽  
Content Type ◽  
Investment Decision Making ◽  
Management Capabilities ◽  
The Impact

Purpose The importance of big data analytics (BDA) on the development of supply chain (SC) resilience is not clearly understood. To address this, the purpose of this paper is to explore the impact of BDA management capabilities, namely, BDA planning, BDA investment decision making, BDA coordination and BDA control on SC resilience dimensions, namely, SC preparedness, SC alertness and SC agility. Design/methodology/approach The study relied on perceptual measures to test the proposed associations. Using extant measures, the scales for all the constructs were contextualized based on expert feedback. Using online survey, 249 complete responses were collected and were analyzed using partial least squares in SmartPLS 2.0.M3. The study targeted professionals with sufficient experience in analytics in different industry sectors for survey participation. Findings Results indicate BDA planning, BDA coordination and BDA control are critical enablers of SC preparedness, SC alertness and SC agility. BDA investment decision making did not have any prominent influence on any of the SC resilience dimensions. Originality/value The study is important as it addresses the contribution of BDA capabilities on the development of SC resilience, an important gap in the extant literature.

Enabling integrated business planning through big data analytics: a case study on sales and operations planning

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alexander Schlegel ◽  
Hendrik Sebastian Birkel ◽  
Evi Hartmann
Keyword(s):  
Big Data ◽  
Information Processing ◽  
Data Analytics ◽  
Single Case ◽  
Big Data Analytics ◽  
Business Planning ◽  
Operations Planning ◽  
Content Type ◽  
The Impact

PurposeThe purpose of this study is to investigate how big data analytics capabilities (BDAC) enable the implementation of integrated business planning (IBP) – the advanced form of sales and operations planning (S&OP) – by counteracting the increasing information processing requirements.Design/methodology/approachThe research model is grounded in the organizational information processing theory (OIPT). An embedded single case study on a multinational agrochemical company with multiple geographically distinguished sub-units of analysis was conducted. Data were collected in workshops, semistructured interviews as well as direct observations and enriched by secondary data from internal company sources as well as publicly available sources.FindingsThe results show the relevancy of establishing BDAC within an organization to apply IBP by providing empirical evidence of BDA solutions in S&OP. The study highlights how BDAC increase an organization's information processing capacity and consequently enable efficient and effective S&OP. Practical guidance toward the development of tangible, human and intangible BDAC in a particular sequence is given.Originality/valueThis study is the first theoretically grounded, empirical investigation of S&OP implementation journeys under consideration of the impact of BDAC.

The influence of the practices of big data analytics applications on bank performance: filed study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hani Al-Dmour ◽  
Nour Saad ◽  
Eatedal Basheer Amin ◽  
Rand Al-Dmour ◽  
Ahmed Al-Dmour
Keyword(s):  
Big Data ◽  
Commercial Banks ◽  
Data Analytics ◽  
Middle Managers ◽  
Big Data Analytics ◽  
Organizational Factors ◽  
Positive Influence ◽  
Bank Performance ◽  
Content Type ◽  
The Impact

Purpose This paper aims to examine factors influencing the practices of big data analytics applications by commercial banks operating in Jordan and their bank performance. Design/methodology/approach A conceptual framework was developed in this regard based on a comprehensive literature review and the Technology–Environment–Organization (TOE) model. A quantitative approach was used, and the data was collected from 235 commercial banks’ senior and middle managers (IT, financial and marketers) using both online and paper-based questionnaires. Findings The results showed that the extent of the practices of big data analytics applications by commercial banks operating in Jordan is considered to be moderate (i.e. 60%). The results indicated that 61% of the variation on the practices of big data analytics applications by commercial banks could be predicated by TOE model. The organizational factors were found the most important predictors. The results also provide empirical evidence that the extent of practices of big data analytics applications has a positive influence on the bank performance. In the final section, research implications and future directions are presented. Originality/value This paper contributes to theory by filling a gap in the literature regarding the extent of the practices of big data analytics applications by commercial banks operating in developing countries, such as Jordan. It empirically examines the impact of the practices of big data analytics applications on bank performance.

scholarly journals Are environmental-related online reviews more helpful? A big data analytics approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marcello Mariani ◽  
Matteo Borghi
Keyword(s):  
Big Data ◽  
North America ◽  
Data Analytics ◽  
Negative Binomial ◽  
Big Data Analytics ◽  
Online Reviews ◽  
Environmental Discourse ◽  
Content Type ◽  
Digital Platforms ◽  
The Impact

Purpose Based on more than 2.7 million online reviews (ORs) collected with big data analytical techniques from Booking.com and TripAdvisor.com, this paper aims to explore if and to what extent environmental discourse embedded in ORs has an impact on electronic word-of-mouth (e-WOM) helpfulness across eight major destination cities in North America and Europe. Design/methodology/approach This study gathered, by means of Big Data techniques, 2.7 million ORs hosted on Booking.com and TripAdvisor, and covering hospitality services in eight different destinations cities in North America (New York City, Miami, Orlando and Las Vegas) and Europe (Barcelona, London, Paris and Rome) over the period 2017–2018. The ORs were analysed by means of ad hoc content analytic dictionaries to identify the presence and depth of the environmental discourse included in each OR. A negative binomial regression analysis was used to measure the impact of the presence/depth of online environmental discourse in ORs on e-WOM helpfulness. Findings The findings indicate that the environmental discourse presence and depth influence positively e-WOM helpfulness. More specifically those travelers who write explicitly about environmental topics in their ORs are more likely to produce ORs that are voted as helpful by other consumers. Research limitations/implications Implications highlight that both hotel managers and platform developers/managers should become increasingly aware of the importance that customer attach to environmental practices and initiatives and therefore engage more assiduously in environmental initiatives, if their objective is to improve online review helpfulness for other customers reading the focal reviews. Future studies might include more destinations and other operationalizations of environmental discourse. Originality/value This study constitutes the first attempt to capture how the presence and depth of hospitality services consumers’ environmental discourse influence e-WOM helpfulness on multiple digital platforms, by means of a big data analysis on a large sample of online reviews across multiple countries and destinations. As such it makes a relevant contribution to the area at the intersection between big data analytics, e-WOM and sustainable tourism research.

Responding to market turbulence by big data analytics and mass customization capability

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hongyan Sheng ◽  
Taiwen Feng ◽  
Lucheng Chen ◽  
Dianhui Chu
Keyword(s):  
Big Data ◽  
Mass Customization ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Two Dimensions ◽  
Content Type ◽  
Managerial Skills ◽  
Service Oriented ◽  
Market Turbulence ◽  
The Impact

PurposeThis study aims to explore how to respond to market turbulence by big data analytics (BDA) capability and mass customization capability (MCC) from the perspective of organizational information processing theory (OIPT).Design/methodology/approachThis study examines the research hypotheses using hierarchical regression analysis by collecting data from 277 Chinese firms.FindingsThe results reveal that supply chain agility (SCA) completely mediates the impacts of technical skills on product-oriented and service-oriented MCC and the impact of data-driven decision-making culture (DDC) on service-oriented MCC. SCA also partially mediates the impacts of managerial skills on two dimensions of MCC and the impact of DDC on product-oriented MCC. In addition, market turbulence strengthens the impact of managerial skills on SCA.Originality/valueThis study provides insightful contributions and implications for enhancing MCC to cope with market turbulence.

Data Preparation for Big Data Analytics

2016 ◽  
pp. 157-170 ◽  
Author(s):  
Andreas Schmidt ◽  
Martin Atzmueller ◽  
Martin Hollender
Keyword(s):  
Big Data ◽  
Real World ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Future Research ◽  
Data Preparation ◽  
Chemical Production ◽  
Specific Project

This chapter provides an overview of methods for preprocessing structured and unstructured data in the scope of Big Data. Specifically, this chapter summarizes according methods in the context of a real-world dataset in a petro-chemical production setting. The chapter describes state-of-the-art methods for data preparation for Big Data Analytics. Furthermore, the chapter discusses experiences and first insights in a specific project setting with respect to a real-world case study. Furthermore, interesting directions for future research are outlined.

Big data analytics in supply chain and logistics: an empirical approach

2018 ◽  
Vol 29 (2) ◽  
pp. 767-783 ◽  
Author(s):  
Maciel Manoel Queiroz ◽  
Renato Telles
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Research Field ◽  
Content Type ◽  
Additional Tool ◽  
Current State ◽  
The Impact ◽  
The Relationship

Purpose The purpose of this paper is to recognise the current state of big data analytics (BDA) on different organisational and supply chain management (SCM) levels in Brazilian firms. Specifically, the paper focuses on understanding BDA awareness in Brazilian firms and proposes a framework to analyse firms’ maturity in implementing BDA projects in logistics/SCM. Design/methodology/approach A survey on SCM levels of 1,000 firms was conducted via questionnaires. Of the 272 questionnaires received, 155 were considered valid, representing a 15.5 per cent response rate. Findings The knowledge of Brazilian firms regarding BDA, the difficulties and barriers to BDA project adoption, and the relationship between supply chain levels and BDA knowledge were identified. A framework was proposed for the adoption of BDA projects in SCM. Research limitations/implications This study does not offer external validity due to restrictions for the generalisation of the results even in the Brazilian context, which stems from the conducted sampling. Future studies should improve the comprehension in this research field and focus on the impact of big data on supply chains or networks in emerging world regions, such as Latin America. Practical implications This paper provides insights for practitioners to develop activities involving big data and SCM, and proposes functional and consistent guidance through the BDA-SCM triangle framework as an additional tool in the implementation of BDA projects in the SCM context. Originality/value This study is the first to analyse BDA on different organisational and SCM levels in emerging countries, offering instrumentalisation for BDA-SCM projects.

Big data analytics with swarm intelligence

2016 ◽  
Vol 116 (4) ◽  
pp. 646-666 ◽  
Author(s):  
Shi Cheng ◽  
Qingyu Zhang ◽  
Quande Qin
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Swarm Intelligence ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Underlying Mechanism ◽  
Research Area ◽  
Future Research ◽  
Content Type

Purpose – The quality and quantity of data are vital for the effectiveness of problem solving. Nowadays, big data analytics, which require managing an immense amount of data rapidly, has attracted more and more attention. It is a new research area in the field of information processing techniques. It faces the big challenges and difficulties of a large amount of data, high dimensionality, and dynamical change of data. However, such issues might be addressed with the help from other research fields, e.g., swarm intelligence (SI), which is a collection of nature-inspired searching techniques. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the potential application of SI in big data analytics is analyzed. The correspondence and association between big data analytics and SI techniques are discussed. As an example of the application of the SI algorithms in the big data processing, a commodity routing system in a port in China is introduced. Another example is the economic load dispatch problem in the planning of a modern power system. Findings – The characteristics of big data include volume, variety, velocity, veracity, and value. In the SI algorithms, these features can be, respectively, represented as large scale, high dimensions, dynamical, noise/surrogates, and fitness/objective problems, which have been effectively solved. Research limitations/implications – In current research, the example problem of the port is formulated but not solved yet given the ongoing nature of the project. The example could be understood as advanced IT or data processing technology, however, its underlying mechanism could be the SI algorithms. This paper is the first step in the research to utilize the SI algorithm to a big data analytics problem. The future research will compare the performance of the method and fit it in a dynamic real system. Originality/value – Based on the combination of SI and data mining techniques, the authors can have a better understanding of the big data analytics problems, and design more effective algorithms to solve real-world big data analytical problems.

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