Trends of Business Model Research: A Bibliometric Analysis

Purpose: The purpose of this article is to provide an overview of the evolution of the business model researchwhile identifying the leading trends and suggesting future research directions.   Design/Methodology/Approach:The study consists of bibliometricanalysis, and bibliographic data visualization using the Web of Science (WoS) database, and clusteranalysis using the VOSViewer software.     Findings:The results reveal the exponential growth of the topic favored within the academic literature. The analysis identified eight clusters of co-words in thefield of the businessmodel (BM). Five relevant research trendswere identified in which the topic of the business model (BM) would developin the next years.   Research limitations:The analysis focuses on the field of management, business, finance, and economics literature. The paper describes the research activity concerninga bibliometric analysis. Therefore it does not take into consideration the quality of the publications and methodological issues.   Practical Implications:This study may serve as a model providing useful information for academic and practitioners to analyzethe topic of the business model (BM) within a certain discipline, as well as to identify research areas that need more attention to come up with theoretical and practical implications.   Originality/Value:The analysis structures and consolidates the concept of the business model (BM) in the academic research, providing valuable insights. It identifies future themes for the development of the fieldand its consolidation within the academic and business literature.   Keywords:Business model research, bibliometrics, co-word analysis, research trends, bibliographic mapping   Classification:Literature Revie

A Study of the Research Hot Topics and Visualization Analysis of Cross-border Ecommerce in China

With many incentive policies recently released, cross-border e-commerce has been highly concerned by all sectors of society. As an emerging research field, it has great research value. Applying SATI to the keywords in CSSCI papers relevant to CBEC from the CNKI periodical database, we undergo bibliometric and visualization study in terms of word frequency analysis. The visualization analysis reveals that: (1) hot topics in CBEC research fall into 4 areas: e-commerce and international business, government policy and supervision, cross-border logistics and cross-border e-commerce finance; (2) prospective research will focus on talent training, synergy, big data, import , customs supervision, etc

The digital transformation of business models in the creative industries: A holistic framework and emerging trends

This paper examines how digital technologies facilitate business model innovations in the creative industries. Through a systematic literature review, a holistic business model framework is developed, which is then used to analyse the empirical evidence from the creative industries. The research found that digital technologies have facilitated pervasive changes in business models, and some significant trends have emerged. However, the reconfigured business models are often not ‘new’ in the unprecedented sense. Business model innovations are primarily reflected in using digital technologies to enable the deployment of a wider range of business models than previously available to a firm. A significant emerging trend is the increasing adoption of multiple business models as a portfolio within one firm. This is happening in firms of all sizes, when one firm uses multiple business models to servedifferent markets segments, sell different products, or engage with multi-sided markets, or to use different business models over time. The holistic business model framework is refined and extended through a recursive learning process, which can serve both as a cognitive instrument for understanding business models and a planning tool for business model innovations. The paper contributes to our understanding of the theory of business models and how digital technologies facilitate business model innovations in the creative industries. Three new themes for future research are highlighted

Research hotspots and trends of fresh e-commerce in China: A knowledge mapping analysis based on bibliometrics

The fresh e-commerce industry has seen a sudden and substantial rise since the outbreak of COVID-19. The rapid development of this industry calls for a comprehensive and systematic review of its research status, hotspots and future trends, which will have significant implications for researchers in related fields. This paper first conducts a current situation analysis of the core literature on fresh e-commerce retrieved from four databases – CNKI, CSSCI, Wanfang and VIP – to categorize the research status of fresh e-commerce in three dimensions: the year of publication, article sources, and distribution of subjects. CiteSpace is then used to perform a bibliometric analysis of the data and to create visualized knowledge maps. The results show that the research on fresh e-commerce can be divided into three stages: rapid development (2012-2015), exploration and transformation (2016-2019), maturity and upgrade (2020-present). At each stage, the research evolves toward diversity and maturity with policy developments and changes in the external environment. Cold chain logistics, business models, freshness-keeping of products and e-commerce are ongoing research hotspots in fresh produce e-commerce, while later studies focus more on the transformation and upgrade of products, logistics, distribution and platforms to better serve consumers’ consumption habits and environmental requirements. This study provides valuable insights for researchers and enterprises who are engaged in the industry and for those who are interested in the development of fresh e-commerce in China

DATA ANALYTICS FOR CRISIS MANAGEMENT: A CASE STUDY OF SHARING ECONOMY SERVICES IN THE COVID-19 PANDEMIC

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Data Analytics for Crisis Management: A Case Study of Sharing Economy Services in the COVID-19 Pandemic

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Twenty Years of Mobile Banking Services Development and Sustainability: A Bibliometric Analysis Overview (2000–2020)

The current paper aims to analyze the keywords related to mobile banking (otherwise known as m-banking) issues by focusing on its development from 2000 to 2020, of which the first publication about this issue appeared in the Scopus database. This paper explored and analyzed 1206 research papers using the Scopus database. Bibliometric analysis and content analysis had been conducted through Excel and VOS viewer software to obtain the results. In addition, the findings of this paper reveal that the universal trends and increased production at a global level led to many changes, and the most rampant topic associated with m-banking in most periods is mobile telecommunication systems. By showcasing the creation of the key terms in m-banking, it was possible to identify significant changes in the development of the field\u27s key terminologies. Therefore, it is important to follow up on the development in future decades, particularly how the recent universal occurrences have influenced the changes in m-banking use at a global level. Moreover, the present study makes a significant contribution to the literature by providing a framework for future research. The framework provides opportunities for researchers to explore the research streams in future research. Finally, the current paper is the first of its kind in its method of contribution, ad according to the research databases (Scopus, Google Scholar, etc.), no work was witnessed in the published literature covering m-banking in a detailed and comprehensive multi-period manner and in such an applied method. In addition, the current paper fills this gap by conducting a bibliometric analysis and content analysis

Intellectual property rights in a knowledge-based economy

Intellectual property rights (IPR) have been created as economic mechanisms to facilitate ongoing innovation by granting inventors a temporary monopoly in return for disclosure of technical know-how. Since the beginning of 1980s, IPR have come under scrutiny as new technological paradigms appeared with the emergence of knowledge-based industries. Knowledge-based products are intangible, non-excludable and non-rivalrous goods. Consequently, it is difficult for their creators to control their dissemination and use. In particular, many information goods are based on network externalities and on the creation of market standards. At the same time, information technologies are generic in the sense of being useful in many places in the economy. Hence, policy makers often define current IPR regimes in the context of new technologies as both over- and under-protective. They are over-protective in the sense that they prevent the dissemination of information which has a very high social value; they are under-protective in the sense that they do not provide strong control over the appropriation of rents from their invention and thus may not provide strong incentives to innovate. During the 1980s, attempts to assess the role of IPR in the process of technological learning have found that even though firms in high-tech sectors do use patents as part of their strategy for intellectual property protection, the reliance of these sectors on patents as an information source for innovation is lower than in traditional industries. Intellectual property rights are based mainly on patents for technical inventions and on copyrights for artistic works. Patents are granted only if inventions display minimal levels of utility, novelty and non-obviousness of technical know-how. By contrast, copyrights protect only final works and their derivatives, but guarantee protection for longer periods, according to the Berne Convention. Licensing is a legal aid that allows the use of patented technology by other firms, in return for royalty fees paid to the inventor. Licensing can be contracted on an exclusive or non-exclusive basis, but in most countries patented knowledge can be exclusively held by its inventors, as legal provisions for compulsory licensing of technologies do not exist. The fair use doctrine aims to prevent formation of perfect monopolies over technological fields and copyrighted artefacts as a result of IPR application. Hence, the use of patented and copyrighted works is permissible in academic research, education and the development of technologies that are complimentary to core technologies. Trade secrecy is meant to prevent inadvertent technology transfer to rival firms and is based on contracts between companies and employees. However, as trade secrets prohibit transfer of knowledge within industries, regulators have attempted to foster disclosure of technical know-how by institutional means of patents, copyrights and sui-generis laws. And indeed, following the provisions formed by IPR regulation, firms have shifted from methods of trade secrecy towards patenting strategies to achieve improved protection of intellectual property, as well as means to acquire competitive advantages in the market by monopolization of technological advances.economics of technology ;

Innovation Systems, Radical Transformation, Step-by-Step: India in Light of China

The paper introduces a reform trajectory we call ?revolutionary incrementalism? in which partial and incremental measures add up to profound transformation. Recent advances in economic theory demonstrate that growth is not hard to start: it almost starts itself, somewhere, sometimes. But keeping it going is not easy: doing so requires attention to the context of growth binding constraints and situation-specific ways to resolve them. The same goes for institutions: it is almost always possible to find some that are working. The issue is using the ones that work to improve those that don?t. The thrust of the proposal is to rely on variation within existing institutions as the ?Archimedean lever? with which to leverage reform and change. India?s public sector record for implementing and coordinating innovation efforts can be notoriously fragmented and inefficient but there are some parts that perform better than others, and there are recognized pockets of excellence virtually within every ministry or public sector organization. The same internal diversity is even more visible in the private sector. Importantly from a policy perspective, better performing segments of public sector and better performing segments of productive sector are beginning to join forces in a variety of search ...innovation systems, heterogeneity of institutions, radical incrementalism, search networks, open economy industrial policy

Bibliometrics and Social Network Analysis of Doctoral Research: Research Trends In Distance Learning

The study investigated research topics of doctoral dissertations that examined issues in distance learning from 2000-2014. Twelve reviews of research on distance learning, spanning from 1997-2015, were identified. It was found that only one of these reviews of research (Davies, Howell, & Petri, 2010) looked at doctoral dissertations. The authors noted that investigating dissertations was complicated and daunting because 1) only a fraction made full text available and 2) there were a large number of dissertations in the area. To counter for these complications the current study utilized bibliometric and social network analysis to investigate dissertation database listings, including abstracts, keywords, classifications, and other bibliographic data. Bibliographic data for dissertation listings (n=3,954) was exported from the ProQuest Dissertations & Theses A&I (PQDT) database. Software developed for the study formatted the data and imported it into a series of databases. Natural language processing techniques were utilized to pull emergent keywords from dissertation abstracts. Department and University types were analyzed. Dissertation reference sections were investigated utilizing co-citation analysis. Author generated keywords and emergent keywords from abstracts were investigated utilizing keyword co-occurrence network analysis. Findings indicated that dissertations came from 17 department types including education-oriented department types, such as Educational Leadership, Educational Technology, and Educational Psychology, as well as non-education-oriented departments, such as Business, Psychology, and Nursing. Seven research topics were found to be pervasive in dissertations from 2000-2014: Student, Instructor, Interaction, Administration and Management, Design, Educational Context, and Technological Medium. No change was found over time; rather these seven topics remained the most central nodes in each of the keyword co-occurrence networks. Finally this method of investigation relied heavily on algorithms developed for the study to aid in data formatting and analysis. The merits of this highly automated SNA approach were discussed. Use of abstracts and natural language processing enabled a much higher n size (n=3954) to be investigated than in comparison with the only other study to analyze distance education dissertations Davies et al. (2010) where n=100. This method enabled the heavy lifting to be dedicated to the interpretation of the results, rather than data preparation