Evaluate the sustainable marketing strategy to optimal online leasing of new energy vehicles under the background big data economy

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tang Daifen
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Online Algorithm ◽  
Content Type ◽  
The Sustainable Development ◽  
Government Subsidies ◽  
New Energy ◽  
Multiple Vehicles ◽  
Sustainable Marketing ◽  
New Energy Vehicle

PurposeUnder the big data background, there are many influencing factors for investors of new energy vehicles (NEV), and government subsidies promote the sustainable development of the new energy vehicle industry. Therefore, the purpose of the study is to provide solutions for the sustainable development of NEV.Design/methodology/approachThe sustainable marketing strategy of NEV in China is put forward. This paper first analyzes the subsidy policy effect of NEV under the background of big data. It then establishes the online optimal leasing strategy under multiple strategy choices and the online leasing strategy of multiple vehicles under the inflation market.FindingsWith the fixed cost of NEV in each lease period, the optimal competition ratio of online decision-makers will continue to decrease with the increase of the difference between prepaid funds and government subsidies. In the decision-making of renting and purchasing multiple vehicles, the general strategy competition ratio is 2.922, while the optimal competition ratio of the online renting and purchasing strategy proposed by the research is 2.723.Research limitations/implicationsThe research is limited by the limited data and information collected, so the optimal decision-making model has some limitations. The authors need to find more representative data to optimize the model.Practical implicationsAs an emerging industry, NEV have developed rapidly in recent years. Based on the online algorithm and competitive ratio theory, this paper solves the decision-making problem of operators and gives the optimal strategy to promote the green development of the new energy vehicle industry.Originality/valueThis paper proposes the optimal strategy for online investors of new energy vehicle operators by combining online algorithm and competitive ratio theory. The numerical analysis results of the optimal online model under multi strategy selection show that with the same difference between prepaid funds and government subsidies, the time point will be delayed and the time point will be advanced as the cost of leasing NEV in each period increases.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

scholarly journals Manager perceptions of big data reliability in hotel revenue management decision making

2019 ◽  
Vol 36 (1) ◽  
pp. 25-39 ◽  
Author(s):  
David Egan ◽  
Natalie Claire Haynes
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Revenue Management ◽  
Management Decision ◽  
Local Market ◽  
Data Systems ◽  
Local Data ◽  
Content Type ◽  
User Input ◽  
Management Decision Making

PurposeThe purpose of this paper is to investigate the perceptions that managers have of the value and reliability of using big data to make hotel revenue management and pricing decisions.Design/methodology/approachA three-stage iterative thematic analysis technique based on the approaches of Braun and Clarke (2006) and Nowell et al. (2017) and using different research instruments to collect and analyse qualitative data at each stage was used to develop an explanatory framework.FindingsWhilst big data-driven automated revenue systems are technically capable of making pricing and inventory decisions without user input, the findings here show that the reality is that managers still interact with every stage of the revenue and pricing process from data collection to the implementation of price changes. They believe that their personal insights are as valid as big data in increasing the reliability of the decision-making process. This is driven primarily by a lack of trust on the behalf of managers in the ability of the big data systems to understand and interpret local market and customer dynamics.Practical implicationsThe less a manager believes in the ability of those systems to interpret these data, the more they perceive gut instinct to increase the reliability of their decision making and the less they conduct an analysis of the statistical data provided by the systems. This provides a clear message that there appears to be a need for automated revenue systems to be flexible enough for managers to import the local data, information and knowledge that they believe leads to revenue growth.Originality/valueThere is currently little research explicitly investigating the role of big data in decision making within hotel revenue management and certainly even less focussing on decision making at property level and the perceptions of managers of the value of big data in increasing the reliability of revenue and pricing decision making.

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.

Government subsidies in the power battery recycling industry

2020 ◽  
Vol 120 (6) ◽  
pp. 1059-1083 ◽  
Author(s):  
Peiqi Ding ◽  
Zhiying Zhao ◽  
Xiang Li
Keyword(s):  
Peer Review ◽  
Vital Role ◽  
Raw Material ◽  
Third Party ◽  
Content Type ◽  
Recycling Industry ◽  
Power Battery ◽  
New Energy ◽  
New Energy Vehicle ◽  
Collection Strategies

PurposeThe power battery is the core of a new energy vehicle and plays a vital role in the rise of the new energy vehicle industry. As the number of waste batteries increases, firms involved in the industry need to properly dispose them, but what party is responsible remains unclear. To reduce environmental impacts, governments introduce two subsidy policies, i.e. collection subsidies, which are provided to the collecting firms, and dismantling subsidies, which are provided to the dismantling firms.Design/methodology/approachBased on the different characteristics of the subsidies, we develop a stylized model to examine the collection strategies and the preferences over the subsidies.FindingsWe derive several insights from analysis. First, the collection strategies depend on the fixed collection cost. Second, the key factor determining the firm's subsidy preference is the efficiency of dismantling. Finally, if the primary target is the collection rate, governments prefer to provide collection subsidies. If consider the environmental impact, the choice of subsidies has to do with the efficiency of dismantling. Moreover, from a social welfare perspective, the raw material cost and the efficiency of dismantling are core indicators of decision.Originality/valueThis work develops the first analytical model to study two power battery subsidies and investigate the optimal collecting strategies and subsidy preferences. The insights are compelling not only for the manufacturer and the third party but also for policymakers.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IMDS-08-2019-0450

Improving the use of analytics and big data by changing the decision-making culture

2017 ◽  
Vol 55 (10) ◽  
pp. 2074-2088 ◽  
Author(s):  
Jane Elisabeth Frisk ◽  
Frank Bannister
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Design Approach ◽  
Action Research Project ◽  
Content Type ◽  
Use Of Data ◽  
Support Organizations ◽  
Novel Approach ◽  
Using Data

Purpose Evolving digital technologies continue to enable new ways to collect and analyze data and this has led some researchers to claim that skillful use of data analytics and big data can radically improve a company’s performance, but that in order to achieve such improvements managers need to change their decision-making culture and to increase the degree of collaboration in the decision-making process. The purpose of this paper is to create an increased understanding of how a decision-making culture can be changed by using a design approach. Design/methodology/approach The paper presents an action research project in which the authors use a design approach. Findings By adopting a design approach organizations can change their decision-making culture, increase the degree of collaboration and also reduce the influence of power and politics on their decision-making. Research limitations/implications This paper proposes a new approach to changing a decision-making culture. Practical implications Using data analytics and big data, a design approach can support organizations change their decision-making culture resulting in better and more effective decisions. Originality/value This paper bridges design and decision-making theory in a novel approach to an old problem.

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.

Anti-virus policies may hurt democracy in eastern EU

2020 ◽  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Health Systems ◽  
Ad Hoc ◽  
Content Type ◽  
East European ◽  
Excessive Demand ◽  
University Level ◽  
East And West ◽  
Back Education

Subject Eastern EU’s handling of COVID-19 pandemic. Significance Central-East European (CEE) authorities are more reactive than proactive on COVID-19 management and have devised an ad hoc patchwork of measures; all are relying on 'stay-at-home' strategies to curb excessive demand on health systems. Politically, COVID-19 is not creating new attitudes but amplifying existing ones. It offers national-populists a fertile environment for centralising decision-making further and adopting measures incompatible with normal democratic standards. Impacts The next EU budget may take into account the latest revelation of less affluent members’ structural weaknesses. However, EU solidarity will be further stretched, creating new tensions between east and west. Although working online is less advanced in most CEE countries, appreciation of and investment in big data and technology will increase. Lockdowns will hold back education, with teachers, even at university level, underprepared to deliver courses remotely.

scholarly journals The Impact of Financial Support on Innovation Efficiency of Government Subsidies From the Perspective of DEA-Tobit——An Empirical Study Based on Chinese New Energy Vehicle Enterprises

2021 ◽  
Author(s):  
Sha Zhang ◽  
Fang Chen
Keyword(s):  
Financial Support ◽  
Negative Impact ◽  
Positive Impact ◽  
Reference Value ◽  
Chinese Government ◽  
Operating Cycle ◽  
Government Subsidies ◽  
New Energy ◽  
New Energy Vehicle ◽  
The Impact

Abstract The new energy vehicle enterprises is a strategic emerging industry in China, so more and more government subsidies to promote innovative development are being accepted by new energy vehicle enterprises. What is the innovation efficiency of new energy vehicle enterprises receiving government subsidies? With the acceleration of the process of global economic financialization, whether financial support can promote the innovation efficiency of government subsidies and how enterprises should allocate financial assets have become issues that need to be deeply considered. Based on the annual report data of China's domestic listed new energy vehicle enterprises from 2015 to 2020, the relationship between government subsidies and enterprise innovation efficiency is empirically tested, and the impact of financial support on enterprise R&D innovation efficiency is investigated. The empirical results show that government subsidies are wasteful and fail to effectively promote R&D innovation, and the innovation efficiency of government subsidies is positively influenced by firm nature and firm age, while the total asset turnover ratio, operating cycle and firm size have a negative impact on innovation efficiency. Further research found that there is an inverted U-shaped relationship between financial support and the innovation efficiency of government subsidies. A certain degree of financial support has a positive impact on the innovation efficiency of government subsidies, but excessive financial support has a negative impact on the innovation efficiency of government subsidies. The conclusion provides empirical evidence for the Chinese government to improve the subsidy policy and standardize the development of new energy vehicle enterprises, and has a certain reference value for guiding new energy vehicle enterprises to reasonably allocate financial support.

scholarly journals Big data and risk management in business processes: implications for corporate real estate

2019 ◽  
Vol 26 (5) ◽  
pp. 1141-1155 ◽  
Author(s):  
Enrico Battisti ◽  
S.M. Riad Shams ◽  
Georgia Sakka ◽  
Nicola Miglietta
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Big Data ◽  
Real Estate ◽  
Business Strategy ◽  
Process Management ◽  
Business Processes ◽  
Content Type ◽  
Data Intensive ◽  
Corporate Real Estate

Purpose The purpose of this paper is to improve understanding of the integration between big data (BD) and risk management (RM) in business processes (BPs), with special reference to corporate real estate (CRE). Design/methodology/approach This conceptual study follows, methodologically, the structuring inter-textual coherence process – specifically, the synthesised coherence tactical approach. It draws heavily on theoretical evidence published, mainly, in the corporate finance and the business management literature. Findings A new conceptual framework is presented for CRE to proactively develop insights into the potential benefits of using BD as a business strategy/instrument. The approach was found to strengthen decision-making processes and encourage better RM – with significant consequences, in particular, for business process management (BPM). Specifically, by recognising the potential uses of BD, it is also possible to redefine the processes with advantages in terms of RM. Originality/value This study contributes to the literature in the fields of real estate, RM, BPM and digital transformation. To the best knowledge of authors, although the literature has examined the concepts of BD, RM and BP, no prior studies have comprehensively examined these three elements and their conjoint contribution to CRE. In particular, the study highlights how the automation of data-intensive activities and the analysis of such data (in both structured and unstructured forms), as a means of supporting decision making, can lead to better efficiency in RM and optimisation of processes.

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