Conference Agenda

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Session Overview
Session
19-AM-07: ST1.1 - How Artificial Intelligence is Reshaping Business Models
Time:
Wednesday, 19/Jun/2019:
8:30am - 10:00am

Session Chair: Gianvito Lanzolla, Cass Business School
Session Chair: Umberto Panniello, Politecnico di Bari
Session Chair: Antonio Messeni Petruzzelli, Politecnico di Bari
Location: Amphi Curie

Session Abstract

Artificial intelligence (AI) is driving changes of business and organizational activities, as well as of the underlying processes and competencies (van der Meulen, 2018), thus attracting the interest from both scholars and practitioners due to its huge impact on processes, products, services, and business models (e.g., Bughin et al., 2017; Dean 2014).

AI refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction. Among the various types of AI solutions, reactive machines and limited memory technologies can recognize objects and make predictions, with the latter using past experiences to inform future decisions (some of the decision-making functions in autonomous vehicles have been designed in this way), while self-awareness AI systems have a sense of self, consciousness and can understand their current state using this information to infer what others are feeling.

The effect of AI technologies is particularly relevant when referred to the business model unit of analysis and, in particular, on the development of new business models or on the changes introduced in existing ones. Recently, the literature seems to converge on defining business model as the “design or architecture of the value creation delivery, and capture mechanisms” of a firm (Teece, 2010). As long with refining the construct of business model and its theoretical and practical relevance (Lanzolla and Markides, 2017), both research and practice realized that business models are subject to innovation in response to changes in their competitive and industrial environment (Chesbrough, 2007; Lindgardt et al., 2009). Innovating a business model does not mean necessarily to introduce a new product, service or technology (Lindgardt et al., 2009), but rather it calls to innovate at least one of its elements, such as the value proposition or the revenue model, thus providing the firm with a new value source that can be used to create a sustainable competitive advantage (Zott and Amit, 2010). Technological change has often been associated with business model innovation and nowadays we have observed a variety of new business models patterns based on the exploitation of AI applications in different industries (e.g., IBM Watson is revolutionizing different sectors, offering novel business opportunities in healthcare, education, weather forecast, fashion, and tax preparation).

We aim at discussing about how AI systems are reshaping business models’ mechanisms, approaches and founding elements (such as organization, infrastructures, customers or value propositions). Specifically, questions include, but are not limited to:

- main managerial and organizational implications related to the adoption of AI in existing business models;

- risks and weaknesses of the adoption of AI in existing business models;

- types and archetypes of AI based business models;

- differences between AI based vs. traditional business models;

- boundary conditions enabling the adoption of AI solutions in existing business models;

- policy-based initiatives and AI based business models;

- the performance implications of adopting AI solutions in incumbents’ or new entrants’ business models;

- antecedents and consequences of the adoption of AI solutions in business models;

- characteristics of the AI solutions that mostly affect business models performance;

- governance mechanisms of business models using AI;

- resources and capabilities underlying the introduction and adoption of AI solutions in business models;

- emerging trade-offs going along with the adoption of AI solutions in business models.


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Presentations

Empirical Study on Customer Loyalty with Intelligent Personal Assistant

CHIA-CHI SUN

TAMKANG U., Taiwan

Context

With the rapid developed advancements in artificial intelligence technologies, the rising demands for the intelligent personal assistant is expected to experience a rapid growth. It is unavoidable for both academic researchers and the marketers to investigate the customer’s responses to the intelligent personal assistant.

Literature

As customer loyalty has become a critical element of gaining competitive advantages and quite a number of marketers pay much attention to it, so this research aims to not only explore the factors which directly affecting customer loyalty on intelligent personal assistant but also trial both mediator and moderator variables.Discussing the aspect of customer innovativeness in the previous research, Li, Zhang and Wang (2015) indicate that the customer innovativeness is the concept about the understanding and adoption of the latest products. Based on Woodruff (1997), the customer value is one of the most important competitive advantage for the companies which beyond quality focus. Moreover, the customer satisfaction would be various caused by the customer knowledge before the purchasing based on the study conducted by Bian and Moutinho (2011).

Literature Gap

In this research, research problems are discussed through various dimensions. In the early stage, it leads to the influences among customer innovativeness, perceived value, customer satisfaction and customer loyalty. The product knowledge and corporate image would be considered as the moderating variables to identify the influences on the early testing.

Research Questions

Whether there is the significant moderating effect through either product knowledge or corporate image on the influence among customer innovativeness, perceived value, customer satisfaction and customer loyalt and the significant mediating effect through customer satisfaction on the influence from either customer innovativeness or perceived value to customer loyalty

Methodology

In this research, the Cronbach’s Alpha method is used to analyze the reliability of each dimension and its items on the purpose of detecting the internal consistency. Before conducting the exploratory factor analysis, KMO (Kaiser-Meyer-Olkin test) and Bartlett’s Test of Sphericity have been used to determine whether the data is available for the factor analysis or not. The hierarchical regression is mainly used to discuss the relations among main variables, mediating variable and moderating variables. Furthermore, the results of regression analysis would be tested based on the adjusted R-Squared, value of Durbin-Watson, VIF and the level of significance.

Empirical Material

Based on the results of testing mediation, the customer satisfaction is concluded as the partial mediator to the influence on customer loyalty originated from customer innovativeness and perceived value. From the view of testing moderation, both product knowledge and corporate image take the role as the moderator variables, which implies the positive moderating effect on the relationship between customer innovativeness and perceived value. Also, product knowledge moderates the influences from functional and hedonic innovativeness to customer satisfaction. From the perspective of demographic factors, Scheffe’s method is applied to find out the significant variances. According to the results of data synthetization and hypothesis testing, several comprehensive strategic analysis and implications will be provided eventually.

Results

This study is going to be considered as the strong presence for existing marketers to strengthen the market position and make the strategic decisions in the global intelligent personal assistant industry.As the intelligent personal assistants are becoming popular and the market is going to be fiercely competitive, the global market provides a great potential benefits for all the developers.From the perspective of the hedonic innovativeness, the promoting content should address the attributes of the intelligent personal assistant such as customers being able to gain the pleasure and enjoyment, especially emphasizing on how it makes the life more exciting and stimulating.

Contribution to Scholarship

It is essential for the companies to be devoted to being familiar with the characteristics of customers such as product knowledge and innovative motivations, the ways to generate the corporate image, create the perceived value and improve overall satisfaction, as the satisfied customers are more likely to be loyal. Therefore, this research aims to be viewed as the specific guide to provide the effective practices that should be implemented in order to help the companies to reallocate the resources appropriately and enhance the customer value creation.

Contribution to Practice

Moreover, it is more appealing to the customers with cognitive innovativeness if the promoting information contains the logical, objective and verifiable descriptions. This kind of customers are more concerned about whether the usage of intelligent personal assistant can challenge their intellectual skills and satisfy the analytical thinking or not. Therefore, for the marketers of intelligent personal assistant, the integration of these three kinds of customer innovativeness can create a higher-level approving perception of product value.

Fitness

With the rapid developed advancements in artificial intelligence, the rising demands for the intelligent personal assistant is expected to experience a rapid growth. It is unavoidable for both academic researchers and the marketers to investigate the customer’s responses to the intelligent personal assistant.

Bibliography

Sun, Chia-Chi is Associate Professor of Department of International Business, Tamkang University, where he has been teaching Technology, Innovation and Operation Research for six years. Sun graduated from National Chia Tung University and received doctoral degree of Technology Management. His research focuses on decision making, entropy theory, and their applications to energy and hi-tech industries



AI as a general purpose technology: Empirical understanding by linked dataset of scientific articles and patents

Kazuyuki Motohashi

University of Tokyo, Japan

Context

This paper investigates the nature of AI, as a general purpose technology, in a sense that generality of its innovation applications, particularly focusing on co-occurrence of publication (free knowledge dissemination) and patent (propriety technology protection).

Literature

AI (machine learning) is described as a general purpose technology, causing various downstream innovations in multiple industries (Agrawal et. al, 2018). In addition, AI can be served as a new method of invention (IMI: Invention as a Method of Inventing), as is seen in AI use for new drug discovery (Cockburn et. al, 2018). At the same time, it is found that the co-occurrence of publication and patenting at individual engineer level is very popular in this field, suggesting that the co-evolution of science and innovation is happening (Motohashi, 2018). Patent citation analysis is a useful tool for analyzing generalizability of invention, and the science linkage and generality is found to be positively correlated (Trajtenberg et. al, 1997). In addition, the combination of (free) publication and (propriety) patent can be understood as a keystone strategy in business ecosystem creation (Iansiti and Levien, 2004).

Literature Gap

Existing studies treats scientific publications and patents separately (Cockburn et. al, 2018), while this paper combines them at individual engineer level to see the importance of such co-evolution of science and innovation. In addition, this study contributes to the understanding business ecosystem by large dataset quantitative analysis.

Research Questions

Why does private firm publish freely their new findings in the field of AI, as well as patenting some of them as propriety technology? Can such behavior be explained by using business ecosystem concept, in a sense that a key stone player needs to encourage subsequent innovations from niche players?

Methodology

Regression analysis is conducted for forward citation and generalization index as a dependent variables. The key explanatory variables are a dummy variable whether such patent is invented by an inventor, who is also an author of scientific publication (co-occurrence of science and innovation at individual engineer level), after controlling for NPL dummy (whether such patent cites non patent literature, mainly scientific publication), the type of applicants (a private firm or a public research organization), application year, technology class etc.

Empirical Material

SCOPUS data from Elsevier for research articles by US researchers and US patent data from USPTO for patents are used. There are around 8 million papers from SCOPUS and 3 million patents from USPTO data. These two data are linked by author/inventor names as well as his/her affiliates, and about 5% of all authors from SCOPUS and about 13.3% of inventors from USPTO data can be linked. The patents which are invented by the authors of AI article (extracted by Elsevier ASJC code = 1702, Artificial Intelligence) are extracted from USPTO patents, and the controlling matched samples are also extracted, based on the same application year and IPC subgroup to the comparing sample (total sample size: 892,384). The regression analysis is conducted for comparing the patents with AI paper author inventor and those without it (controlling sample) at patent level.

Results

It is found that patents with AI paper author inventor have significantly higher forward citations (both self and non-self citation), as well as generality index (representing the diversity of technology classes of citing patents, used as an index for general purpose nature), as compared to matched controlling samples. This result is persistent even after controlling for the NPL (non patent literature) citation, which implies that the general purpose nature of inventions based on AI publication comes not only from science linkage of such invention (NPL citation), but also from particular characteristics of AI publication as a source of substantial subsequent innovations. In addition, higher forward citation and generality index are found both in patents by public research institute and private firms. This finding (these patents attract substantial subsequent innovations, even for private firm’s ones) is consistent with the hypothesis that a firm uses open publication of AI article as a tool to attract niche players (subsequent innovations) based on its business eco-system strategy.

Contribution to Scholarship

This paper look into the nature of AI, as a source of subsequent innovations in variety of application field (general purpose technology) by the dataset of research articles and patents. The nature of AI, causing fundamental economics and social changes, are discussed mainly with anecdotal qualitative stories. However, this paper takes a systematic approach of rigorous quantitative analysis based on novel dataset. Another academic contribution of this paper is providing empirical evidence of business ecosystem formulation, focusing on strategic use of publication (making scientific achievements on AI open to the public) as a tool for attracting niche players.

Contribution to Practice

Contribution to practice has two folds. One is to provide managerial implications to firms in terms of business ecosystem strategy. Particularly, the empirical results in this paper imply that publication of research article can base used a strategic use for ecosystem formation. Another practical implication is on human resource management for innovative outputs. Patents with higher subsequent innovations are invented by those who have publication in AI field as well. Therefore, development of cross over talents between science and engineering is important for firm’s HR development strategy.

Fitness

This paper features AI driven innovation, and analyzes how it changes business innovation landscape. Therefore, I suppose the fitness to the theme is quite high.

Bibliography

Agrawal. A., J.. Gans and A. Goldfarb (2018), Prediction Machines: The Simple Economics of Artificial Intelligence, Harvard Business School Press, April 2018

Cockburn, I., Henderson, R. and S. Stern (2018), The impact of artificial intelligence on innovation, NBER working paper #24449, March 2018

Iansiti, M. and R. Levien (2004), Strategy as Ecology, Harvard Business Review, March 2004

Jaffe, A. and G. de Rassenfosse (2017), Patent citation data in social science research: Overview and best practices, Journal of the Association for Information Science and Technology, , 68(6):1360–1374, 2017

Motohashi, K. (2018), Co-occurrence of science and innovation in AI : Empirical analysis of paper-patent linked dataset in the United States, NISTEP working paper No. 160 (in Japanese)

Trajtenberg, M., Henderson, R., & Jaffe, A. (1997). University versus corporate patents: A window on the basicness of invention. Economics of Innovation and New Technology, 5(1), 19–50



Understanding startups’ dynamics: big data approach

Maksim Malyy, Zeljko Tekic

Skolkovo Institute of Science and Technology (Skoltech), Russian Federation

Context

In contrast to publicly trading companies, startups do not have to share publicly any data and they do not have much data to share. So, most of the time researchers are not able to collect enough data for reaching statistically significant or even trustworthy results.

Literature

By now, no study used Google Trends for analyzing startups’ dynamics. Thus, our primary research goal is to validate and demonstrate how Google Trends can be used as a tool for analyzing startups dynamic. We root our logic on existing evidence. For example, Goel et al. [2] showed that search query volume could be used to forecast the opening weekend box-office revenue for feature films, first month sales of video games, and the rank of songs on the Billboard Hot 100 chart. Choi and Varian [3] found that GT is especially useful for forecasting economic activity (e.g. car sales, home sales, retail, and travel) as GT data helps identifying the "turning point" that occurs suddenly in the market. This seems especially true when information is lacking on a specific product due to the novelty of its brand [4].

Literature Gap

Since 2006, more than 600 scientific studies employing Google Trends (GT) to reveal hidden dependencies [5] and “predict the present” [3]. We aim to propose and discuss the methodology of utilizing GT for understanding dynamics of new ventures that was not done previously.

Research Questions

Our research aims to find answers on the following questions:

- Is Google Trends a convenient instrument to understand the dynamics of startups?

- How reliable are these data?

- How can Google Trends data be analyzed in order to get previously hidden dependencies?

Methodology

Development patterns observed in the statistical curves of startups’ Google Trends search queries reflect their real dynamics and signal about the occurrence of the "turning point" that in the case of a startup may be business model validation or product-market fit (PMF) [6].

We focus on the most successful startups – group known as “unicorns”. Next to using Google Trends, we collect various important events from companies’ timelines, like the launch of the product in the public market, etc., and correlate them to the GT data. We also approximate GT curves with various functions and present the results.

Empirical Material

According to the proposed research methodology, empirical data for the sample companies were collected from the Google Trends web engine. The sample included approx. 100 most valuable unicorns located in US and taken from various sources, e.g. https://www.cbinsights.com/research-unicorn-companies. After excluding the outliers, we got approx. 50 cases, where we observed the proposed development pattern.

Results

In the sample of 50 unicorns, we observe a clear development pattern, which could be shortly described as a “flat-peak-growth.” The flat part reflects the first stage of company development and is characterized by the absence of significant changes in its dynamics. Google Trends data of this part can be approximated by a straight line with a small or even without any slope. The second part of the pattern is the first peak point. It follows the flat part and reflects the sudden multiple increases of the public interest into term behind which is a specific startup. This point on curve correlates with some certain timeline event like posting of the MVP video for the case of Dropbox [8]. The trend to continuous exponential growth follows this point and stops when the company reaches its maturity. The “flat-peak-growth” pattern plays an essential role in the analysis of startups’ dynamics, because ability to identify predecessor of exponential growth makes it possible to analyze other timeline events relatively to it and to make conclusions about startups’ strategies. Taking into account the after-peak growth, we may assume that startup has reached business model validation or PMF somewhere close to this first peak.

Contribution to Scholarship

We believe that proposed method will be useful for scholars who examine startups and their dynamics from different angles: economic, social, marketing, intellectual property, strategic, etc. It brings an objective way of measuring and tracking the dynamics of a particular startup company resulting in potential to have qualitatively new results in understanding the new ventures management.

Contribution to Practice

Proposed methodology will help practitioners who work with startups (for instance, venture analysts, business angels, VC funds managers, etc.) in the form of providing a reliable analytical tool. It can add value for the investment decision-making process, for tracking the progress of the portfolio companies and the effectiveness of their marketing campaigns.

Fitness

Google Trends is known as a Big Data instrument, therefore from our point of view, our research fits well the topic of the current conference, especially the Track 1.2 - The Data Science of Startups.

Bibliography

[1] S. Blank, “Why the Lean Start-Up Changes Everything,” 2013. [Online]. Available: https://hbr.org/2013/05/why-the-lean-start-up-changes-everything. [Accessed: 11-Nov-2018].

[2] S. Goel, J. M. Hofman, S. Lahaie, D. M. Pennock, and D. J. Watts, “Predicting consumer behavior with Web search.,” Proc. Natl. Acad. Sci. U. S. A., vol. 107, no. 41, pp. 17486–90, Oct. 2010.

[3] H. Choi and H. Varian, “Official Google Research Blog: Predicting the Present with Google Trends,” Google Corp, 2009.

[4] H. Assael, Consumer behavior and marketing action. Kent Pub. Co., 1984.

[5] S. P. Jun, H. S. Yoo, and S. Choi, “Ten years of research change using Google Trends: From the perspective of big data utilizations and applications,” Technol. Forecast. Soc. Change, vol. 130, no. November, pp. 69–87, 2018.

[6] B. Cooper and P. Vlaskovits, The entrepreneur’s guide to customer development : a “cheat sheet” to The four steps to the epiphany. 2010.

[7] W. Gornall and I. Strebulaev, “Squaring Venture Capital Valuations with Reality,” vol. 1, no. 604, pp. 1–54, 2017.

[8] “Digg - Google Drive killer coming from MIT Startup,” 2008. [Online]. Available: https://web.archive.org/web/20080314093228/http://digg.com/software/Google_Drive_killer_coming_from_MIT_Startup. [Accessed: 07-Mar-2019].



Research on the Influence of Business Model Contextualization on Value Creation: Digital Transformation of RAINBOW from 2007 to 2018

Ji-hai JIANG, Chun–hua CAI, Wei LIU

Chongqing University, China, People's Republic of

Context

In the rapidly changing VUCA era (Volatility, Uncertainty, Complexity, Ambiguity), digital technology is reshaping the business world. Traditional business logic is difficult to maintain a sustainable competitive advantage. Especially in China’s unique digital environment, consumer demand has changed dramatically. Therefore, the digitalization of business models has become an inevitable trend.

Literature

AMIT, R.,C.ZOTT. VALUE CREATION IN E-BUSINESS[J]. Strategic Management Journal, 2001,22: 493-520.

Amit, R.,X. Han. Value Creation through Novel Resource Configurations in a Digitally Enabled World[J]. Strategic Entrepreneurship Journal, 2017,11:228-242.

Feng Li. The digital transformation of business models in the creative industries:A holistic framework and emerging trends[J]. https://doi.org/10.1016/j.technovation.2017,12.004

Spieth, P. Exploring the Linkage between Business Model (&) Innovation and the Strategy of the Firm[J]. R & D Management, 2016,3(46): 403-413.

Pagani, M. and C. Pardo. The impact of digital technology on relationships in a business network[J]. Industrial Marketing Management, 2017,67:185-192.

YANG De-ming, LIU Yong-wen.Why can Internet Plus Increase Performance[J].China’s Industrial Economics,2018, (5):80-98.

Literature Gap

By combing the existing literature, we found the process, the path, characteristics of the business model digitization and its impact on value creation are rarely studied. That is, how the business model is digitized and the impact on business performance is still a “black box”.

Research Questions

(1) What are the value drivers driving the digitization of business models?

(2) What are the paths for digitalization of business models? And how does it affect value creation?

Methodology

Case Study and Grounded Theory

Case study is considered to be the most suitable tools to construct a new theory in an early stage(Yin,1984; Eisenhardt,1989 & 2007). Single case study can be used to research the typical cases, the conclusions from this case will help deepen the understanding of the similar events(Yin, 2003). And rooted analysis is also an important method of qualitative research, its core process is to turn data into the concept and create theory, through analyzing and comparing the collected materials constantly.

Empirical Material

Three squirrels founded in 2012 is the first pure internet company in snack industry, its sales ranked first in four consecutive years. The company has received about RMB 500 million of venture capital, the market value is more than 4 billion yuan.We collected materials about the company through its official website, the prospectus, databases like CNKI, 3-5 interviews, news media interviews, newspapers and magazines, Baidu search engine and etc. There are 825 items in all related to the research topic, we encoded data with different characters according to different sources as follows: A(120 items) is from the company's news and media coverage; B(192 items) is from the announcement on official website; C(106 items) is from databases like CNKI; D(56 items) is from search engine like Baidu; E(112 items) is from new medias like WeChat Official Account and Microblog; V(116 items) is videos from search engine like Baidu and Tencent ect.

The data collection of this case study follows "Trib of evidence" rule, which forms a triangular verification between the data from different sources, thus improving the reliability and validity of the study, making the conclusion of this study persuasive and accurate.

Results

According to the defined business model concept and rooted analysis of the case of Three squirrels, the four paths of business model digitization are summarized from user identification dimension, transaction content dimension, transaction structure dimension and transaction governance dimension: user digitization, key business dataization, key cooperation network and management online. And the study may find that there is a a U-shaped or positive correlation between business model digitization and corporate performance.

Contribution to Scholarship

(1) Different from the novel business model and efficient business model proposed by Amit and Zott, this paper proposes a digital business model and creatively constructs “user digitization→key business dataization→key cooperation network→management online→enterprise performance/value creation”. The theoretical framework of value creation enriches existing research on business models.

(2) Through longitudinal case study of Three Squirrels and rooted analysis, this papar may identify four paths of digitization of business models, and demonstrate the mechanism of the impact on value creation.

(3) In terms of research content and conclusions, this paper not only opens up the black box of how the business model is digitized, but also finds that the relationship with performance maybe U-shaped or positive .

Contribution to Practice

(1) When enterprises find business opportunities through identifying users, they should use digital technologies such as big data, and artificial intelligence to achieve more accurate positioning and demand matching to improve customer satisfaction and promote corporate performance. For example, through in-depth analysis of the users’ lifestyle, personality characteristics and consumption scenes, and 360-degree accurate images, enterprises can achieve accurate user identification and provide personalized services.

(2) Data has become the new and most important production factor in the era of digital economy. Enterprises need to manage their key business data, and truly achieve refinement and precision.

Fitness

The theme of this paper is how digital technology can reshape the business model of the enterprise, with China's unique digital environment as the background. Therefore, it highly relevant to the Theme 1 of the conference: Artificial Intelligence & Data science; Track 1.1 How Artificial Intelligence is Reshaping Business Models.

Bibliography

AMIT, R.,C.ZOTT. VALUE CREATION IN E-BUSINESS[J]. Strategic Management Journal, 2001,22: 493-520.

Amit R., Zott C.. Crafting Business Architecture: The Antecedents of Business Model Design[J]. Strategic Entrepreneurship Journal,2015,9(4): 331-350.

Amit, R.,X. Han. Value Creation through Novel Resource Configurations in a Digitally Enabled World[J]. Strategic Entrepreneurship Journal, 2017,11:228-242.

Feng Li. The digital transformation of business models in the creative industries:A holistic framework and emerging trends[J]. https://doi.org/10.1016/j.technovation.2017,12.004

Spieth, P. Exploring the Linkage between Business Model (&) Innovation and the Strategy of the Firm[J]. R & D Management, 2016,3(46): 403-413.

Pagani, M. and C. Pardo. The impact of digital technology on relationships in a business network[J]. Industrial Marketing Management, 2017,67:185-192.

YANG De-ming, LIU Yong-wen.Why can Internet Plus Increase Performance[J].China’s Industrial Economics,2018, (5):80-98.

Osterwalder, A. Business Model Generation[J]. 2009, Europe: Amsterdam: Privately publishe.

Clauss T.. Measuring Business Model Innovation: Conceptualization, Scale Development, and Proof of Performance[J]. R & D Management, 2017, (forthcoming).

Chesbrough H.. Business Model Innovation: Opportunities and Barriers[J]. Long Range Planning,.2010,43(2-3): 354-363.

Chandler, J.,S. Vargo. Contextualization and Value-in-Context: How Context Frames Exchange[J]. Marketing Theory, 2011,(11):35-49.

德勤. 中国零售企业数字化转型成熟度评估报告. 德勤研究报告, 2017.

埃森哲. 创新驱动,高质发展 中国企业数字转型指数. 2018.

罗珉,李亮宇. 互联网时代的商业模式创新:价值创造视角[J].中国工业经济, 2015(1): 95-107.

麦肯锡. 中国的数字化转型:互联网对生产力和增长的影响(简). 2014.

陈春花,廖建文. 数字化时代企业生存之道[J]. 哈佛商业评论中文版, 2018.

魏炜,朱武祥. 商业模式经济解释[M]. 2015: 机械工业出版社.



 
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