What attracts vehicle consumers’ buying

2019 ◽  
Vol 120 (1) ◽  
pp. 57-78 ◽  
Author(s):  
Fuli Zhou ◽  
Ming K. Lim ◽  
Yandong He ◽  
Saurabh Pratap
Keyword(s):  
Big Data ◽  
Operations Management ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Content Type ◽  
Consumption Behavior ◽  
Integrated Method ◽  
Data Analytic

Purpose The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

The framework of talent analytics using big data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arnold Saputra ◽  
Gunawan Wang ◽  
Justin Zuopeng Zhang ◽  
Abhishek Behl
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Talent Management ◽  
Content Type ◽  
Using Data ◽  
Business Challenges ◽  
Practical Implications

PurposeThe era of work 4.0 demands organizations to expedite their digital transformation to sustain their competitive advantage in the market. This paper aims to help the human resource (HR) department digitize and automate their analytical processes based on a big-data-analytics framework.Design/methodology/approachThe methodology applied in this paper is based on a case study and experimental analysis. The research was conducted in a specific industry and focused on solving talent analysis problems.FindingsThis research conducts digital talent analysis using data mining tools with big data. The talent analysis based on the proposed framework for developing and transforming the HR department is readily implementable. The results obtained from this talent analysis using the big-data-analytics framework offer many opportunities in growing and advancing a company's talents that are not yet realized.Practical implicationsBig data allows HR to perform analysis and predictions, making more intelligent and accurate decisions. The application of big data analytics in an HR department has a significant impact on talent management.Originality/valueThis research contributes to the literature by proposing a formal big-data-analytics framework for HR and demonstrating its applicability with real-world case analysis. The findings help organizations develop a talent analytics function to solve future leaders' business challenges.

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.

Analytics based decision making

2014 ◽  
Vol 6 (4) ◽  
pp. 332-340 ◽  
Author(s):  
Deepak Agrawal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Design Methodology ◽  
Historical Perspective ◽  
Big Data Analytics ◽  
Analytical Techniques ◽  
Content Type ◽  
Informed Decisions ◽  
Different Types

Purpose – This paper aims to trace the history, application areas and users of Classical Analytics and Big Data Analytics. Design/methodology/approach – The paper discusses different types of Classical and Big Data Analytical techniques and application areas from the early days to present day. Findings – Businesses can benefit from a deeper understanding of Classical and Big Data Analytics to make better and more informed decisions. Originality/value – This is a historical perspective from the early days of analytics to present day use of analytics.

scholarly journals Ambidextrous organization and agility in big data era

2018 ◽  
Vol 24 (5) ◽  
pp. 1091-1109 ◽  
Author(s):  
Riccardo Rialti ◽  
Giacomo Marzi ◽  
Mario Silic ◽  
Cristiano Ciappei
Keyword(s):  
Big Data ◽  
Conceptual Framework ◽  
Data Analytics ◽  
Design Methodology ◽  
Business Process Management ◽  
Process Management ◽  
Big Data Analytics ◽  
Organizational Agility ◽  
Content Type ◽  
Practical Implications

Purpose The purpose of this paper is to explore the effect of big data analytics-capable business process management systems (BDA-capable BPMS) on ambidextrous organizations’ agility. In particular, how the functionalities of BDA-capable BPMS may improve organizational dynamism and reactiveness to challenges of Big Data era will be explored. Design/methodology/approach A theoretical analysis of the potential of BDA-capable BPMS in increasing organizational agility, with particular attention to the ambidextrous organizations, has been performed. A conceptual framework was subsequently developed. Next, the proposed conceptual framework was applied in a real-world context. Findings The research proposes a framework highlighting the importance of BDA-capable BPMS in increasing ambidextrous organizations’ agility. Moreover, the authors apply the framework to the cases of consumer-goods companies that have included BDA in their processes management. Research limitations/implications The principal limitations are linked to the need to validate quantitatively the proposed framework. Practical implications The value of the proposed framework is related to its potential in helping managers to fully understand and exploit the potentiality of BDA-capable BPMS. Moreover, the implications show some guidelines to ease the implementation of such systems within ambidextrous organizations. Originality/value The research offers a model to interpret the effects of BDA-capable BPMS on ambidextrous organizations’ agility. In this way, the research addresses a significant gap by exploring the importance of information systems for ambidextrous organizations’ agility.

Impacting Big Data analytics in higher education through Six Sigma techniques

2017 ◽  
Vol 66 (5) ◽  
pp. 662-679 ◽  
Author(s):  
Chad Laux ◽  
Na Li ◽  
Corey Seliger ◽  
John Springer
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Student Success ◽  
Research Design ◽  
Six Sigma ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Content Type ◽  
Conceptual Research

Purpose The purpose of this paper is to develop a framework for utilizing Six Sigma (SS) principles and Big Data analytics at a US public university for the improvement of student success. This research utilizes findings from the Gallup index to identify performance factors of higher education. The goal is to offer a reimagined SS DMAIC methodology that incorporates Big Data principles. Design/methodology/approach The authors utilize a conceptual research design methodology based upon theory building consisting of discovery, description, explanation of the disciplines of SS and Big Data. Findings The authors have found that the interdisciplinary approach to SS and Big Data may be grounded in a framework that reimagines the define, measure, analyze, improve and control (DMAIC) methodology that incorporates Big Data principles. The authors offer propositions of SS DMAIC to be theory tested in subsequent study and offer the practitioner managing the performance of higher education institutions (HEIs) indicators and examples for managing the student success mission of the organization. Research limitations/implications The study is limited to conceptual research design with regard to the SS and Big Data interdisciplinary research. For performance management, this study is limited to HEIs and non-FERPA student data. Implications of this study include a detailed framework for conducting SS Big Data projects. Practical implications Devising a more effective management approach for higher education needs to be based upon student success and performance indicators that accurately measure and support the higher education mission. A proactive approach should utilize the data rich environment being generated. The individual that is most successful in engaging and managing this effort will have the knowledge and skills that are found in both SS and Big Data. Social implications HEIs have historically been significant contributors to the development of meritocracy in democratic societies. Due to a variety of factors, HEIs, especially publicly funded institutions, have been under stress due to a reduction of public funding, resulting in more limited access to the public in which they serve. Originality/value This paper examines Big Data and SS in interdisciplinary effort, an important contribution to SS but lacking a conceptual foundation in the literature. Higher education, as an industry, lacks penetration and adoption of continuous improvement efforts, despite being under tremendous cost pressures and ripe for disruption.

Davenport and Prusak on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 7-11 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Early Years ◽  
Content Type ◽  
Potential Risks ◽  
The Relationship

Purpose Larry Prusak and Tom Davenport have long been leading voices in the knowledge management (KM) field. This interview aims to explore their views on the relationship between KM and big data/analytics. Design/methodology/approach An interview was conducted by email with Larry Prusak and Tom Davenport in 2015 and updated in 2016. Findings Prusak and Davenport hold differing views on the role of KM today. They also see the relationship between KM and big data/analytics somewhat differently. Davenport, in particular, has much to say on how big data/analytics can be best utilized by business as well as its potential risks. Originality/value It is important to understand how two of the most serious KM thinkers since the early years of KM understand the relationship between big data/analytics, KM and organizations. Their views can help shape thinking in these fields.

Does big data mean big knowledge? KM perspectives on big data and analytics

2017 ◽  
Vol 21 (1) ◽  
pp. 1-6 ◽  
Author(s):  
David J. Pauleen ◽  
William Y.C. Wang
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Content Type ◽  
Guiding Principle ◽  
Proposed Model ◽  
Organizational Value ◽  
Practical Implications

Purpose This viewpoint study aims to make the case that the field of knowledge management (KM) must respond to the significant changes that big data/analytics is bringing to operationalizing the production of organizational data and information. Design/methodology/approach This study expresses the opinions of the guest editors of “Does Big Data Mean Big Knowledge? Knowledge Management Perspectives on Big Data and Analytics”. Findings A Big Data/Analytics-Knowledge Management (BDA-KM) model is proposed that illustrates the centrality of knowledge as the guiding principle in the use of big data/analytics in organizations. Research limitations/implications This is an opinion piece, and the proposed model still needs to be empirically verified. Practical implications It is suggested that academics and practitioners in KM must be capable of controlling the application of big data/analytics, and calls for further research investigating how KM can conceptually and operationally use and integrate big data/analytics to foster organizational knowledge for better decision-making and organizational value creation. Originality/value The BDA-KM model is one of the early models placing knowledge as the primary consideration in the successful organizational use of big data/analytics.

Improving emergency response operations in maritime accidents using social media with big data analytics: a case study of the MV Wakashio disaster

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Carine Dominguez-Péry ◽  
Rana Tassabehji ◽  
Lakshmi Narasimha Raju Vuddaraju ◽  
Vikhram Kofi Duffour
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Emergency Response ◽  
Early Warning System ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Warning System ◽  
Content Type

PurposeThis paper aims to explore how big data analytics (BDA) emerging technologies crossed with social media (SM). Twitter can be used to improve decision-making before and during maritime accidents. We propose a conceptual early warning system called community alert and communications system (ComACom) to prevent future accidents.Design/methodology/approachBased on secondary data, the authors developed a narrative case study of the MV Wakashio maritime disaster. The authors adopted a post-constructionist approach through the use of media richness and synchronicity theory, highlighting wider community voices drawn from social media (SM), particularly Twitter. The authors applied BDA techniques to a dataset of real-time tweets to evaluate the unfolding operational response to the maritime emergency.FindingsThe authors reconstituted a narrative of four escalating sub-events and illustrated how critical decisions taken in an organisational and institutional vacuum led to catastrophic consequences. We highlighted the specific roles of three main stakeholders (the ship's organisation, official institutions and the wider community). Our study shows that SM enhanced with BDA, embedded within our ComACom model, can better achieve collective sense-making of emergency accidents.Research limitations/implicationsThis study is limited to Twitter data and one case. Our conceptual model needs to be operationalised.Practical implicationsComACom will improve decision-making to minimise human errors in maritime accidents.Social implicationsEmergency response will be improved by including the voices of the wider community.Originality/valueComACom conceptualises an early warning system using emerging BDA/AI technologies to improve safety in maritime transportation.

Big Data Analytics and IoT in logistics: a case study

2018 ◽  
Vol 29 (2) ◽  
pp. 575-591 ◽  
Author(s):  
John Hopkins ◽  
Paul Hawking
Keyword(s):  
Big Data ◽  
Fuel Consumption ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Driver Safety ◽  
Logistics Industry ◽  
Content Type ◽  
The World ◽  
Maintenance Schedules

Purpose Advances in technology enable companies to collect and analyse data, which were previously not accessible, to either enhance existing business processes or create new ones. The purpose of this paper is to document the role and impact of Big Data Analytics (BDA), and the Internet of Things (IoT), in supporting a large logistics firm’s strategy to improve driver safety, lower operating costs, and reduce the environmental impact of their vehicles. Design/methodology/approach A single case with embedded units intrinsic case study method was adopted for this research and data were collected from a “real-life” situation, to create new knowledge about this emerging phenomenon. Findings Truck telematics were utilised in order to better understand, and improve, driving behaviours. Remote control centres monitor live sensor data from the company’s fleet of vehicles, capturing the likes of speed, location, braking, and engine data, to inform future training programs. A combination of truck telematics and geo-information are being used to enable proactive alerts to be sent to drivers regarding possible upcoming hazards. Camera-based technologies have been adopted to improve driver safety, and fatigue management, capturing evidence of important driving events and storing data directly to the cloud, and BDA is also being used to improve truck routing, recommend optimal fuel purchasing times/locations, and to forecast predictive and proactive maintenance schedules. Research limitations/implications The type of data collected by Company A, and similar logistics companies, has the potential to greatly inform researchers investigating autonomous vehicles, smart cities, and the physical internet. Practical implications Eco-driving, a practice informed/improved by BDA at Company A, has been linked to reductions in fuel consumption and CO2 emissions, which bring both economic and environmental benefits. Technologies similar to Truckcam are growing in popularity in some parts of the world, to the point where it is now common practice to use dashcam assess of accidents to establish liability. This has implications for logistics firms, in other parts of the world, where such practices might not yet be so commonplace, and for drivers and society more broadly. Social implications Improvements in utilisation and routing have the potential to reduce traffic congestion, which is responsible for losses in productivity, increases in fuel consumption, air pollution and noise, and can incite stress, aggression, anger and unsafe behaviours in drivers. Predictive analytics, which generate refuelling and maintenance schedules, have the potential to be adopted by all vehicle manufacturers, and could generate reductions in customer fuel costs, whilst improving the performance, efficiency, and life expectancy of future motor all vehicles. The high probability of occupations in the logistics industry being replaced by computer automation in the near future is also discussed. Originality/value The findings from this research serve as a valuable case example of a real-world deployment of BDA and IoT technologies in the logistics industry, and present implications for practitioners, researchers, and society more widely.

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

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