business enterprise research paper

Digital Entrepreneurship in Business Enterprises: A Systematic Review

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• Samuel Anim-Yeboah 14 ,
• Richard Boateng   ORCID: orcid.org/0000-0002-9995-3340 14 ,
• Emmanuel Awuni Kolog   ORCID: orcid.org/0000-0002-6924-3532 14 ,
• Acheampong Owusu   ORCID: orcid.org/0000-0001-7789-5162 14 &
• Ibrahim Bedi 14  

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12066))

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This study systematically reviews extant contemporary literature on digital entrepreneurship in peer-reviewed journal articles over six years (2013–2018) from six journal databases. It involved a systematic literature review of 101 papers from 53 journals focusing on the publication outlets, yearly trends, themes, and associated theoretical and conceptual approaches, methodologies, sources and geographical distribution of digital entrepreneurship research. The findings suggest that extant literature mostly lacked sound theoretical underpinnings. More work adopting appropriate and proven theoretical approaches is needed. Most of the reviewed papers also focused mainly on issues relating to the technology itself than those relating to the enterprise or the entrepreneur. The capabilities and capacities of enterprises, as well as the strategies in implementing digital technologies and harnessing the opportunities of digitalization, are key issues that have not hitherto received much attention. The study contributes to the understanding of the conceptualization of the digital entrepreneurship phenomenon. Future research should consolidate the understanding of the field, with models and frameworks that recognize digital entrepreneurship as an academic research field in its own right, and also consider the impact of enterprise capabilities and capacities on digital entrepreneurship.

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• Digital technology
• Digital entrepreneurship
• Business enterprise
• Systematic review

1 Introduction

Digital entrepreneurship is generally defined as the pursuit of business or economic opportunities based on the use of digital technologies [ 9 ], and this definition is adopted for this study. The entrepreneurs involved in the digital entrepreneurship are then described as digital entrepreneurs while the resulting ventures or firms, which provide economic and social value for themselves or their communities, are referred to as digital enterprises [ 9 , 50 ]. Although researchers and policymakers have widely used the term ‘digital entrepreneurship’, its conceptualisation remains elusive, with very little evidence of scholarship in the field [ 50 ].

There is a growing interest in digital entrepreneurship since it is considered to be the ultimate and contemporary trend in entrepreneurship development due to the rapid development of digital technologies and the emerging digital economy [ 20 ]. Bogdanowicz [ 2 ] also emphasises the renewed and increasing interest in digital entrepreneurship and calls for empirical evidence [ 24 ].

Despite the increased interest in digital entrepreneurship and technology-based innovations, there has been limited clarification of the concept from different perspectives and conceptualisations. Moreover, there has also been a lack of contextual and conceptual development and discussion of the concept of digital entrepreneurship, as most prior research examined only the sporadic phenomena associated with it [ 42 , 50 ]. Furthermore, some critical and fundamental issues about digital entrepreneurship currently remain unresolved in the literature. These include how digital technologies transform entrepreneurship, how digital entrepreneurship predicts performance outcomes, and how digital entrepreneurship differs from traditional entrepreneurship [ 48 , 50 ]. Meanwhile, not much has been done in terms of reviewing the body of literature and research trends in digital entrepreneurship, so the need for conceptualisations in the field is much desired [ 34 ].

Furthermore, there is a dearth in knowledge regarding the detailed classification of digital technology-enabled entrepreneurship and enterprises, making it difficult to appreciate the current level of understanding and boundaries of the original concept [ 36 , 41 ]. Additionally, the current conceptualisation of digital entrepreneurship is considerably diverse. While some researchers have opted for a broad conceptualisation of digital entrepreneurship as a combination of digital technology and entrepreneurship innovation [ 1 , 14 ], others have limited the concept to the attainment of entrepreneurship goals with digital technological applications [ 44 ].

It is essential to review achievements and studies to date, regarding what has been done, what needs to be revisited and what is still missing in the field, in order to better appreciate and promote the development of digital entrepreneurship on the academic and research front [ 33 ]. A review of studies on the concept of digital entrepreneurship is necessary to evaluate the current understanding of, and complementary perspectives on, how the digital technology revolution has permeated entrepreneurship and innovation [ 22 ].

Hence, compelled by the challenges posed by the development of the digital entrepreneurship concept, particularly in the IS research environment, this study seeks to provide a systematic review of the extant literature on digital entrepreneurship. The study will identify and describe the major issues, themes, trends, distribution, and focus of research on the concept. It will also examine the methodological and theoretical approaches to past studies on the concept, identify the limitations and gaps in the literature, and offer recommendations for future research. The resulting review is expected to serve as a one-stop source, offering insight into what has been accomplished so far, what is currently being done, and what challenges and opportunities lie ahead, in terms of research on digital entrepreneurship. The study, therefore, addresses the following questions to achieve this:

What are the major trends, characteristics, and distribution of research work on digital entrepreneurship?

What major issues and themes are being focused on and discussed in digital entrepreneurship research?

What theoretical, conceptual, and methodological, approaches are being used to address digital entrepreneurship research?

What are the limitations and gaps in the extant literature on digital entrepreneurship?

This study seeks to systematically review research articles concerning digital entrepreneurship in peer-reviewed journals from six major journal databases, over six years. The next section presents the methodology employed, while the third section presents the findings and discussions. The fourth section elaborates the limitations and gaps identified, followed by the conclusion and contribution.

2 Methodology

The study was conducted as a systematic literature review (SLR) of extant studies on the conceptualisation of digital entrepreneurship [ 37 ]. The searches for articles were conducted in six electronic databases for which the researcher had full-text access: ScienceDirect/Elsevier, Emerald, AIS Library, Sage, Springer, and Taylor and Francis. Although these databases may not exhaustively list all relevant journals, they, however, cover a reasonable portion of the existing database for IS journals. As Levy and Ellis, [ 29 ] noted in their guide to a systematic approach to a literature review in IS, it is better to use multiple databases in conducting literature searches, since the IS domain is multidisciplinary and IS literature outlets are highly diversified. Quality IS literature is dispersed through-out hundreds of databases and some of the databases used in this study, being multidisciplinary, are among those recommended by Levy and Ellis, [ 29 ] as useful for IS research. Moreover, most journals in these databases are globally top-ranked IS journals [ 4 ]. Against this backdrop, the list of databases above was a fair and adequate representation of the relevant IS databases suited for digital entrepreneurship study, which is a multidisciplinary subject.

The searches were conducted using “digital innovation” and “digital entrepreneurship” as search terms. Other keywords included “digital enterprise,” “digital economies,” “digital technologies,” and “innovative technologies.” The search was limited to articles published between January 2013 and August 2018, resulting in 175 papers or articles, that were downloaded.

The exclusion criteria applied included the delimiting of the papers to peer-reviewed research articles, and hence, conference papers and book chapters were excluded from the study, in addition to stock reviews. The articles were also restricted to those concerning business entrepreneurship and business enterprises, and thus, all articles concerning policy, education, and social entrepreneurship were eliminated. After the papers had been identified and elicited, they were sorted and cross-checked to eliminate duplications.

Ultimately, 101 articles from 53 journals were selected. The selected papers were then classified based on the publishing journal, year of publication, digital technology issues and themes, theoretical and conceptual approaches and frameworks, research methodologies and methods, data sources and levels of analysis. The data collected on the various classifications were analysed and summarized using descriptive statistics.

3 Findings and Discussions

3.1 publication databases and journals.

Regarding the distribution of the articles in the databases, it was found that Emerald hosted the majority of the publications, followed by ScienceDirect, Springer Link, and Taylor and Francis. Sage and AIS Library, by contrast, had small numbers of journals and articles, with particularly the AIS Library having the least. Such observation may be explained by the fact that, because digital entrepreneurship researches straddle multidisciplinary fields, it may be expected that libraries and databases that accommodate multidisciplinary fields will have more articles on digital entrepreneurship [ 29 ]. Moreover, apart from the AIS Library, which contains papers specifically related to IS, the other libraries (Emerald, ScienceDirect, Taylor and Francis, Springer Link and Sage) contain papers from several different fields [ 29 ].

In terms of the number of articles per journal, the Journal of Small Business and Enterprise Development had the highest number of 11 (10.9%) articles and is followed by Technological Forecasting and Social Change with 9 (8.9%) articles. The Information and Management journal had 5 (4.9%) articles , Journal of Business Research and the Journal of Information Technology had 4 (4.0%) articles each, while the Journal for Innovation and Entrepreneurship, Journal of Strategic Marketing, Small Business Economics journal and the Journal of Open Innovation, Technology, Market and Complexity had 3 (3.0%) articles each. Twelve (12) of the journals had 2 (2.0%) articles each while the rest of the journals, thirty-two (32) in all, had 1 (1.0%) article each.

The focal areas for many of the journals from which the papers were obtained included information systems (IS), information technology (IT), innovation, business, entrepreneurship, management, marketing, human relations, governance, regulation, operation, production, knowledge, planning, strategy, gender, and other diverse fields. These focal areas suggest the suitability of the journals for digital entrepreneurship research, which is a multidisciplinary concept that is applied in different scientific and academic fields. It further corroborates the observation that digital entrepreneurship is multidisciplinary in perspective [ 5 , 45 , 47 ].

3.2 Year of Publication

The distribution of the publications by year (Fig.  1 ) shows an increasing trend of articles on digital entrepreneurship from January 2013 to August 2018. Throughout the period under study, the number of papers published continually increased from six papers in 2013 to 33 papers in 2018 (up to August only), representing more than five-fold increment. Apart from an insignificant drop from six in 2013 to five in 2014, the increment was consistent, as the number of papers increased to 13 in 2015, 14 in 2016, 30 in 2017, and 33 in 2018. This trend shows that the number of publications is likely to increase further in the future.

Trends in the publication by year (N = 101)

This trend shows a growing interest in digital entrepreneurship, not only in practice and policy but also in research, which affirms Kelestyn and Henfridsson’s [ 27 ] claim. Considering the proliferation of digital technology and information technology (IT) based business, the increase in digital entrepreneurship research is expected to continue for several years [ 19 , 27 , 30 ].

3.3 Focus and Categories of Research Issues and Themes

The study identified several issues of focus in the papers reviewed. These issues were grouped into four categories as (i) those that focused directly on the technology involved (73 papers), (ii) those that focused on the relationships and interactions with the technology (58 papers) (iii) those that focused directly on the enterprise (53 papers), and (iv) those that focused on the entrepreneur (13 papers).

The technology - focused issues concerned digital technologies like mobile technology, e-business platforms, social media, cloud computing, big data, crowdsourcing, internet, enterprise systems, and blockchain [ 49 ]. Some of the technology-focused articles, however, did not indicate any specific technologies but mentions digital technologies or ICT in general. The interaction - focused issues comprised access, adoption, impact, role, influence, possession, trust, and use of digital technology. The enterprise - focused issues involved demographics, business model, innovation, transformation, performance, productivity, profitability, value, expansion, growth, convergence, ecosystems, incubations, start-ups, cooperation, competition, internationalisation, marketing, stakeholder collaboration, success factors, and strategic orientation. The entrepreneur - focused issues encompassed behaviour, gender, competence, perception, positions, and management.

Given that digital entrepreneurship is ICT-driven [ 35 ], it was not surprising that the technology-focused issues (from 73 papers) dominated the various issues addressed. The distribution of the themes within the technology focussed issues shows that most of the papers, 33 articles (32.7%) did not specify the exact technology theme. Specifically, the dominant technology theme identified from the publications on digital entrepreneurship included e-business platforms with 12 (11.9%) papers and social media platforms with 10 (9.9%) papers. Other digital platforms and mobile technology reflected in 4 (4.0%) papers each while cloud computing had 3 (3.0%) papers. The enterprise systems and blockchain were considered in 2 (2.0%) papers, whereas internet service, big data, and crowdsourcing reflected in 1 (1.0%) paper each. The e-Business, social media, other digital platforms, and mobile application are all expected to feature as main themes in publications on digital entrepreneurship due to their popularity. Of the 58 papers that focused on issues of the interactions with technology, 17 (16.8%) focused on influence, 13 (12.9%) on adoption, 11 (10.9%) on impact, 7 (6.9%) on use, 6 (5.9%) on role, 2 (1.6%) on trust and 1 (1.0%) each on access to and possession of the technology. Whereas, out of the 53 papers with enterprise-focused issues 17 of them focused on business model, innovation and transformation; 10 on business growth, expansion, performance, success factors and return on investment; 7 on ecosystems, incubation and sharing economy, 6 on competition, convergence, collaboration and cooperation; 5 on enterprise processes, institutional and social interactions; 4 on enterprise state, demographics, boundaries and employment; and 4 on marketing and strategy. Meanwhile, of the 13 papers focusing on entrepreneur related issues, 5 focused on the entrepreneur’s perspectives, perceptions and behaviour; 3 on entrepreneur’s gender, race and class; 3 on entrepreneurial competence and another 3 on ownership and management. These constitute the trending issues that dominate contemporary discussions and study of digital entrepreneurship.

3.4 Theoretical and Conceptual Approaches

Regarding the use of specific theories or concepts, the findings show that 57 (56.4%) of the papers had no theory or concept underpinning it, while 44 (43.6%) had theories or frameworks. Of the 44 papers that were underpinned by theories or concepts, 35 (34.6%) used single theories, while 9 (8.9%) combined two or three theories. Meanwhile, 14 (13.9%) of the 35 papers that used single theory or concept and 1 (1.0%) of the nine papers that combined theories or concepts utilized the author’s frameworks. This implies that only 29 (28.7%) of the papers, comprising 21 (20.8%) of the 35 papers with single theory and 8 (7.9%) of the nine papers with combined theories, utilized known and established theories or concepts.

In all 28 different known and established theories and concepts were employed. The Dynamic Capability theory was used in 4 papers, while the Resource-Based View was used in 3 papers. Diffusion of Innovation, Institutional Theory, Technology Acceptance Model, Technology Organization and Environment framework, Theory of Planned Behavior, and the Trust Theory were used in two papers each, while each of the remaining 20 theories was used in one paper each. The theory-based studies focused on enterprise-related issues such as business model, value, process, innovation, and transformation, as well as competition, expansion, marketing, and strategic orientation. The review also shows that some of the studies with no theories could have been underpinned with applicable theories in IS literature such as social theories, socio-technical theories, institutional theories and the Task-Technology Fit (TTF) theoretical framework which could explain the assumptions on which many of the publications were conducted [ 6 , 38 , 43 ].

These theories and concepts that were used by the papers reflect the wide application of different theories and concepts in digital entrepreneurship for different purposes, depending on the focus of the research, which emphasizes the multidisciplinary nature of digital entrepreneurship with a diversity of research approaches. With fewer papers, 29 (28.7%) in all, utilizing 28 different established theories and concepts also suggests the newness of the knowledge area in research. For studies in digital entrepreneurship to gain prominence in the IS research landscape, further work based on grounded, appropriate, and credible theoretical approaches should be considered.

Among the theories used, the most dominant was the social theories approach with 25 (24.8%) papers, followed by the socio-technical theories approach with 11 (10.9%) papers and the technical theories approach with 5 (4.9%) papers. The dominance of social theories could be due to the social nature of entrepreneurship studies. Many of the studies that used social theories focused on ICT adoption and impact [ 11 , 18 , 39 ], while others used the theories to explain the influence and impact of ICT on business [ 8 , 12 , 40 ]. From the review, it also emerged that studies that adopted the socio-technical theories approach focused on the influence of ICT in business [ 6 ], the extent of ICT adoption, and barriers to its adoption [ 38 ]. The theories applied in the publications reviewed are popular and prominent in IS research.

3.5 Research Methodology Used and Trend

Regarding the classification of the publications by the research methodology employed, four distinct groups emerged, namely, those that used mixed methods, qualitative methodology, quantitative methodology, and those with no defined methodology. Regarding the distribution, the results show the almost equal distribution for studies using qualitative (41 (40.6%) papers) and quantitative methodologies (39 (38.6%) papers), which were the dominant approaches. Just a few papers used mixed methods 3 (3.0%), while a reasonable number did not use any defined methodology 18 (17.8%).

Of the 41 quantitative papers, 22 (21.8%) were based on theories, of which 15 (14.9%) were established theories. Whereas, of the 39 qualitative papers, 17 (16.8%) were based on theories, of which 12 (11.9%) were established theories. Having 83 (82.2%) of the papers with both quantitative and qualitative methodologies almost equally shared suggest that the digital entrepreneurship research is becoming more mature with proven methodologies. The 18 studies that were not underpinned by any defined methodology were made of 16 reviews, and two content analysis papers and most did not have any theory as well. Moreover, with 13 of the 18 no defined methodology and review papers published in 2017 and 2018, it goes to suggest the growing interest in exploring the research already done in the area of digital entrepreneurship.

It was further observed that both quantitative and qualitative methodologies have increased in use in recent years (Fig.  2 ). The increasing use of qualitative methodology suggests the subjection of digital entrepreneurship to exploratory research, being a new field, while the increasing dominance of quantitative methodology also reveals the simultaneous development of statistical rigor and analysis [ 21 , 39 ]. According to Creswell [ 7 ], qualitative methods are commonly used in fields that are new and require more exploratory research designs, hence the observed trend reflects the attractiveness of digital entrepreneurship as a new area of research.

The yearly trend of research methodology

3.6 Data Sources, Research Methods and Levels of Analysis

The data sources used in the publications included secondary sources, primary sources, and some undefined sources. The results indicate that about two-thirds, 68 (67.3%) of the papers used data from primary sources, whereas about a quarter, 27 (26.7%) of the papers used secondary data sources. The data sources available for a study largely depend on the availability of previous studies on the topic and the research approach employed. The survey and case study research approaches tend to employ primary sources [ 12 , 31 , 32 ], whereas most reviews tend to rely on secondary sources [ 10 , 46 ].

Based on the results of the study, survey and case study were the two dominant methods used, which resulted in the use of primary data sources for many of the papers. Meanwhile, the data sources and methods used are also influenced by the level of research development in the field of inquiry [ 25 ]. In relatively new areas of research, obtaining secondary data or the adoption of a literature review approach may be hampered. The limited use of secondary data sources in many of the works published in this area reflects the relative newness of the field. This assertion is also reflected in the research approach that was employed in the study. From the results, it is clear that the most widely used methods were a survey, case study, literature review, and interviews. The use of the case studies and interviews suggest that more exploratory and descriptive questions are being asked at the same time that rigor is being sought through surveys. The greater use of the research approaches mentioned above, compared to others, suggests that many of the papers were focused on discovering and describing phenomena related to digital entrepreneurship [ 25 ]. Interestingly of the 35 papers that used a case study or interview methods, 21 of them have no theory base, which further emphasizes the exploratory and descriptive purposes. The focus of such papers included adoption, impact, influence and use of digital technologies like social media, e-business, enterprise systems and crowdsourcing [ 15 , 28 ].

Research methods, approaches, and data sources used may also be entirely dependent on the level of analysis that the researcher intends to perform [ 3 ]. The results show that many of the studies were conducted at the micro-level, with very few at the macro- and meso- levels, which suits entrepreneurship research [ 25 ]. The current focus on organizational level noted in the study may be explained by the fact that many of the studies are applied studies that do not simply seek to advance knowledge, but also to understand, contextualize and explore the practices of entrepreneurs and SMEs that have adopted digital innovation technologies [ 13 , 16 , 17 , 23 , 26 , 38 ].

4 Limitations, Gaps and Future Research

The main limitation of the study was the restriction of the electronic databases to only six, namely AIS Library, Emerald, Sage, ScienceDirect/Elsevier, Springer Link, and Taylor and Francis Online. Another limitation is the restriction of the study to articles published between 2013 and 2018. Such limitations would result in some appropriate articles being eluded. Future research could expand on the database and also consider other forms of digital entrepreneurship.

Only a few of the studies discussed had sound theoretical underpinnings. A major gap identified, is the limited use of theoretical and conceptual frameworks that would bring the concept of digital entrepreneurship up to par with major areas of academic inquiry in IS research. Meanwhile, for studies in digital entrepreneurship to acquire some eminence in the IS research purview, more work adopting appropriate and proven theoretical approaches is needed. Few papers focused on issues relating to the enterprise while much fewer papers focused on the entrepreneur. Future research should consider the drivers and motivation of the entrepreneur for digital entrepreneurship. The capabilities and capacities of enterprises, as well as the strategies in implementing digital technologies and harnessing the opportunities of digitalization, are key issues that have not hitherto received much attention.

Future research should consolidate the understanding of the field, with models and frameworks that recognize digital entrepreneurship as an academic research field in its own right, and also consider the impact of enterprise capabilities and capacities on digital entrepreneurship.

5 Conclusion and Contribution

There is an arguable case for acknowledging digital entrepreneurship as a distinct field that has attracted considerable scholarly attention in recent years. As a multidisciplinary and multi-sectoral subject, research on digital entrepreneurship encompasses many fields. Owing to the broad range of research within the scope of digital entrepreneurship, the understanding of the concept is diffuse and open to misinterpretation.

The paper provides guidance for researchers with insight into the conceptualization of digital entrepreneurship as a multidisciplinary research field. It will also help academicians understanding a holistic view of available research and the developing trend in digital entrepreneurship. This paper contributes to information systems research by describing and classifying the published literature in digital entrepreneurship and by pointing out the gaps where further research is most needed. Furthermore, the paper provides a framework that may provide a conceptual structure for future studies in digital entrepreneurship.

Beckman, C.M., Eisenhardt, K., Kotha, S., Meyer, A., Rajagopalan, N.: The role of the entrepreneur in technology entrepreneurship. Strateg. Entrepreneurship J. 6 (3), 203–206 (2012)

Article   Google Scholar  

Bogdanowicz, M.: Digital entrepreneurship barriers and drivers-the need for a specific measurement framework. Institute for Prospective Technological Studies, European Commission, Joint Research Centre (JRC) Technical report, EUR 27679 EN (2015). https://doi.org/10.2791/3112

Bryman, A., Bell, E.: Business Research Methods. Oxford University Press, Oxford (2015)

Google Scholar  

CABS: Acad. J. Guide (AJG). Chart. Assoc. Bus. Schools (2018). https://charteredabs.org/academic-journal-guide-2018/

Carlborg, P., Kindström, D., Kowalkowski, C.: The evolution of service innovation research: a critical review and synthesis. Serv. Ind. J. 34 (5), 373–398 (2014)

Chandna, V., Salimath, M.S.: Peer-to-peer selling in online platforms: a salient business model for virtual entrepreneurship. J. Bus. Res. 84 , 162–174 (2018)

Creswell, J.W.: Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 2nd edn. Sage, Thousand Oaks (2003)

Daniel, E.M., Domenico, M.D., Sharma, S.: Effectuation and home-based online business entrepreneurs. Int. Small Bus. J. 33 (8), 799–823 (2015)

Davidson, E., Vaast, E.: Digital entrepreneurship and its socio-material enactment. In: 43rd Hawaii International Conference on System Sciences (HICSS) 2010, pp. 1–10. IEEE (2010)

David-West, O., Umukoro, I.O., Onuoha, R.O.: Platforms in Sub-Saharan Africa: start-up models and the role of business incubation. J. Intell. Cap. 19 (3), 581–616 (2018)

Durkin, M., McGowan, P., McKeown, N.: Exploring social media adoption in small to medium-sized enterprises in Ireland. J. Small Bus. Enterp. Dev. 20 (4), 716–734 (2013)

Dutot, V., Bergeron, F.: From strategic orientation to social media orientation: improving SMEs’ performance on social media. J. Small Bus. Enterp. Dev. 23 (4), 1165–1190 (2016)

Ensign, P.C., Farlow, S.: Serial entrepreneurs in the Waterloo ecosystem. J. Innov. Entrepreneurship 5 (1), 20 (2016)

Ferreira, J.J.M., Ferreira, F.A.F., Fernandes, C.I.M.A.S., Jalali, M.S., Raposo, M.L., Marques, C.S.: What do we [not] know about technology entrepreneurship research? Int. Entrepreneurship Manag. J. 12 (3), 713–733 (2016). https://doi.org/10.1007/s11365-015-0359-2

Foroudi, P., Gupta, S., Nazarian, A., Duda, M.: Digital technology and marketing management capability: achieving growth in SMEs. Qual. Mark. Res.: Int. J. 20 (2), 230–246 (2017)

Fu, X., Mohnen, P., Zanello, G.: Innovation and productivity in formal and informal firms in Ghana. Technol. Forecast. Soc. Change 131 , 315–325 (2018)

Garcia-Murillo, M., Velez-Ospina, J.A.: ICTs and the informal economy: mobile and broadband roles. Digital Policy Regul. Gov. 19 (1), 58–76 (2017)

George Wynn, M., Turner, P., Lau, E.: E-business and process change: two case studies (towards an assessment framework). J. Small Bus. Enterp. Dev. 20 (4), 913–933 (2013)

Giones, F., Brem, A.: Digital technology entrepreneurship: a definition and research agenda. Technol. Innov. Manag. Rev. 7 (5), 44–51 (2017)

Hafezieh, N., Akhavan, P., Eshraghian, F.: Exploration of process and competitive factors of entrepreneurship in digital space: a multiple case study in Iran. Educ. Bus. Soc.: Contemp. Middle Eastern Issues 4 (4), 267–279 (2011)

Haghighi, N.F., Hajihoseini, H., Nargesi, G.R., Bijani, M.: Gap analysis of current and desired states of entrepreneurship development components in the field of ICTs in Iran. Technol. Soc. 54 , 101–110 (2018)

Hanna, R., Rohm, A., Crittenden, V.L.: We’re all connected: the power of the social media ecosystem. Bus. Horiz. 54 (3), 265–273 (2011)

Hartmann, P.M., Zaki, M., Feldmann, N., Neely, A.: Capturing value from big data–a taxonomy of data-driven business models used by start-up firms. Int. J. Oper. Prod. Manag. 36 (10), 1382–1406 (2016)

Hull, C.E.K., Hung, Y.T.C., Hair, N., Perotti, V., DeMartino, R.: Taking advantage of digital opportunities: a typology of digital entrepreneurship. Int. J. Netw. Virtual Organ. 4 (3), 290–303 (2007)

Ireland, R.D., Reutzel, C.R., Webb, J.W.: Entrepreneurship research in AMJ: what has been published, and what might the future hold? Acad. Manag. J. 48 (4), 556–564 (2005)

Kabongo, J.D., Okpara, J.O.: ICT possession among Congolese SMEs: an exploratory study. J. Small Bus. Enterp. Dev. 21 (2), 313–326 (2014)

Kelestyn, B., Henfridsson, O.: Everyday digital entrepreneurship: the inception, shifts, and scaling of future-shaping practices. In: Proceedings of the 35th International Conference on Information Systems (ICIS 2014). Auckland, New Zealand (2014)

Kuester, S., Konya-Baumbach, E., Schuhmacher, M.C.: Get the show on the road: go-to-market strategies for e-innovations of start-ups. J. Bus. Res. 83 , 65–81 (2018)

Levy, Y., Ellis, T.J.: A systems approach to conduct an effective literature review in support of information systems research. Inf. Sci. J. 9 , 181–212 (2006)

Miniaoui, H., Schilirò, D.: Innovation and entrepreneurship for the growth and diversification of the GCC economies, MPRA Paper No. 71898 (2016). https://mpra.ub.uni-muenchen.de/71898/ . Accessed 20 Nov 2018

Mohajerani, A., Baptista, J., Nandhakumar, J.: Exploring the role of social media in importing logics across social contexts: the case of IT SMEs in Iran. Technol. Forecast. Soc. Change 95 , 16–31 (2015)

Moghavvemi, S., Mohd Salleh, N.A., Standing, C.: Entrepreneurs adoption of information system innovation: the impact of individual perception and exogenous factors on entrepreneur’s behavior. Internet Res. 26 (5), 1181–1208 (2016)

Mosey, S., Guerrero, M., Greenman, A.: Technology entrepreneurship research opportunities: insights from across Europe. J. Technol. Transf. 42 (1), 1–9 (2017)

Nambisan, S.: Digital entrepreneurship: toward a digital technology perspective of entrepreneurship. Entrepreneurship Theory Pract. 41 (6), 1029–1055 (2017)

Ngoasong, M.Z.: Digital entrepreneurship in a resource-scarce context: a focus on entrepreneurial digital competencies. J. Small Bus. Enterp. Dev. 25 (3), 483–500 (2018)

Ngoasong, M., Paton, R., Korda, A.: Impact investing and inclusive business development in Africa: a research agenda. The Open University, Milton Keynes, IKD Working Paper, No. 76 (2015). http://oro.open.ac.uk/42157/1/ikd-working-paper-76.pdf . Accessed 18 Dec 2018

Okoli, C.: A guide to conducting a standalone systematic literature review. Commun. Assoc. Inf. Syst. 37 (43), 879–910 (2015)

Panayiotou, N.A., Katimertzoglou, P.K.: Micro firms internet adoption patterns: the case of the Greek jewellery industry. J. Enterp. Inf. Manag. 28 (4), 508–530 (2015)

Sarmah, B., Sharma, S., Gupta, S.: Antecedents of e-business adoption intention: an empirical study. Int. J. Innov. Sci. 9 (4), 417–434 (2017)

Sedera, D., Lokuge, S., Grover, V., Sarker, S., Sarker, S.: Innovating with enterprise systems and digital platforms: a contingent resource-based theory view. Inf. Manag. 53 (3), 366–379 (2016)

Shemi, A.P., Procter, C.: E-commerce and entrepreneurship in SMEs: the case of myBot. J. Small Bus. Enterp. Dev. 25 (3), 501–520 (2018)

Shen, C.H., Chen, S.C., Hsiao, H.C., Chang, J.C., Chou, C.M., Chen, C.P.: Can the entrepreneurship course improve the entrepreneurial intentions of students? Int. Entrepreneurship Manag. J. 11 (3), 557–569 (2015)

Tumbas, S., Berente, N., Seidel, S., vom Brocke, J.: The ‘digital facade’ of rapidly growing entrepreneurial organizations. In: Proceedings of the International Conference on Information Systems (ICIS 2015), Fort Worth, Texas (2015)

Wallin, A., Still, K., Henttonen, K.: Entrepreneurial growth ambitions: the case of finnish technology start-ups. Technol. Innov. Manag. Rev. 6 (10), 5–16 (2016)

Whittington, D.: Digital Innovation and Entrepreneurship. Cambridge University Press, Cambridge (2018)

Book   Google Scholar  

Xiao, X., Califf, C.B., Sarker, S., Sarker, S.: ICT innovation in emerging economies: a review of the existing literature and a framework for future research. J. Inf. Technol. 28 (4), 264–278 (2013)

Yoo, Y., Henfridsson, O., Lyytinen, K.: Research commentary—the new organizing logic of digital innovation: an agenda for information systems research. Inf. Syst. Res. 21 (4), 724–735 (2010)

Yunis, M., Tarhini, A., Kassar, A.: The role of ICT and innovation in enhancing organizational performance: the catalyzing effect of corporate entrepreneurship. J. Bus. Res. 88 , 344–356 (2018)

Zalan, T.: Born global on blockchain. Rev. Int. Bus. Strategy 28 (1), 19–34 (2018)

Zhao, F., Collier, A.: Digital entrepreneurship: research and practice. In: Proceedings of the 9th Annual Conference of the EuroMed Academy of Business, 2016, Warsaw, Poland (2016)

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Samuel Anim-Yeboah, Richard Boateng, Emmanuel Awuni Kolog, Acheampong Owusu & Ibrahim Bedi

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Anim-Yeboah, S., Boateng, R., Awuni Kolog, E., Owusu, A., Bedi, I. (2020). Digital Entrepreneurship in Business Enterprises: A Systematic Review. In: Hattingh, M., Matthee, M., Smuts, H., Pappas, I., Dwivedi, Y., Mäntymäki, M. (eds) Responsible Design, Implementation and Use of Information and Communication Technology. I3E 2020. Lecture Notes in Computer Science(), vol 12066. Springer, Cham. https://doi.org/10.1007/978-3-030-44999-5_16

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• Published: 03 July 2021

Research on enterprise business model and technology innovation based on artificial intelligence

• Sunping Qu 1 , 2 ,
• Hongwei Shi 1 ,
• Huanhuan Zhao 2 ,
• Lin Yu 2 &
• Yunbo Yu 2  

EURASIP Journal on Wireless Communications and Networking volume  2021 , Article number:  145 ( 2021 ) Cite this article

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Small- and medium-sized enterprises (SEMs) are the important part of economic society whose innovation activities are of great significance for building innovative country. In order to investigate how technological innovation (TI) and business model design (BMD) affect the business performance of SMEs, samples of 268 SMEs in the artificial intelligence industry and hierarchical regression models are used in the analysis. The results indicate that TI, BMD, and the matching of them have different effects on the innovation of SMEs of different sizes. These findings are helpful for enriching the theory of the fit between TI and BMD and providing theoretical guidance for the innovation activities in SEMs.

1 Introduction

As COVID-19 sweeps through the world, the world economy is hit hard. But many companies in the field of artificial intelligence (AI) have achieved positive growth. In particular, many small- and medium-sized enterprises (SEMs) in the AI industry have grown rapidly. As an important part of economy, SEMs are numerous and provide a large number of jobs. They play an irreplaceable role in world economy. In order to survive and develop in the new crown epidemic, SEMs have a strong impetus for innovation. Innovation includes not only technological innovation (TI) but also business model design (BMD). TI, however, does not necessarily bring profits to enterprises, which also needs to combine with BMD. Chesbrough & Rosenbloom stated that TI can bring profits to enterprises due to BMD plays an important role [ 1 ]. BMD promotes the transformation and upgrading in China. Business model is an important approach of transforming TI into commercial value. Without business model transforming innovative technology into the products and services which customers really want, TI is just a waste of money, which cannot be a driving force for the healthy development of enterprises. Therefore, SMEs should proceed with TI and meanwhile implement the business model which is compatible with the TI.

TI and BMD are the hotspots of academic research. Scholars have put forward some meaningful research results, but the following research gaps still exist. First, from the perspective of research content, although the existing literature analyzed the effects of the fit between TI and BMD on enterprise performance, few studies take the types of TI and BMD into account. Second, from the perspective of research object, the existing literature studied the fit between TI and BMD in large enterprises, but paid little attention to SEMs. Existing research shows that there is a difference between the innovation of large enterprises and that of small and medium enterprises. Therefore, the academic community should research the innovation behaviors in SEMs [ 2 ]. Third, from the perspective of research methods, the main research method in existing literature is case study. Case study is good at discovering new theories, but whether the results of the case analysis are universal remains to be further tested [ 3 ]. However, the existing studies lack quantitative research, especially the research of the interaction effect of TI and BMD on BP in SEMs which is still at an infant stage [ 4 ]. To fill up these research gaps, this study collects data from 268 SEMs in the artificial intelligence industry in China with questionnaire survey, and uses hierarchical regression analysis to explore the effects of different types of TI and BMD and the fit between the two on BP in SEMs.

The key contributions of this work are:

By analyzing the existing literature, this work summarizes the impact of technological innovation, business model design, and the interaction between the two on enterprise innovation performance, and clarifies whether these conclusions are applicable to SMEs, which needs to be further tested.

This paper collects data with questionnaires, uses empirical analysis to study the impact of technological innovation, business model design, and the interaction of the two on the innovation performance of SMEs, and considers the impact of enterprise scale.

Based on the results of the empirical analysis, the article puts forward theoretical guidelines for the implementation of technological innovation and business model design activities for SMEs.

The remainder of the paper is organized as follows. In the next section, we review the literature on TI, BMD and BP that is relevant to our context, and develop our hypotheses. In Sect.  3 , we describe our empirical approach and outline our sample of firms. In Sect.  4 , this paper provides our results of hypotheses testing. In Sect.  5 , we discuss the theoretical contributions and managerial implications and also provide promising areas for future research.

2 Related work

Drawing on the research of Zott et al., this paper assumes that the trading environment contains three participants: core company, customer, supplier or partner. F refers to core company, which is the research object. i represents the i-th supplier or partner. The value of i ranges from 1 to I , which indicates that there is a total of I suppliers or partners. t represents the t-th transaction, which ranges from 1 to T . It means that there is a total of T types of transactions. n(t) represents the volume of the t-th transaction. The gains that the core company F receives from a particular transaction “ t ” is expressed by the following formula:

In which, VF ( t ) is the income that core company F gets from a single specific transaction t . P ( t ) is the fee paid by the customer to the core company F in the t th transaction. Ri ( t ) is the income that the core company F received from the i th supplier or partner in the t th transaction. \(\sum\limits_{i = 1}^I {{R_i}(t)}\) is the sum of the income of core company F from all suppliers or partners in the t th transaction. C i ( t ) is the fee paid by the core company F to the i th supplier or partner in each transaction. \(\sum\limits_{i = 1}^I {{C_i}(t)}\) is the fee paid by the core company F to suppliers or partners in the t th transaction. CF ( t ) is the cost of the core company in the t th transaction, including financial costs and intellectual costs [5].

The total value ( TV ) of the core company F in the business model is:

In which, n ( t ) is the volume of the t-th transaction. The total income of the company is positively affected by product/service price (P(t)), company’s income from partners and suppliers ( Ri ( t )), quantity of transaction types ( T ), and volume of the t th transaction ( n ( t )), and is negatively affected by the cost consumed by partners and suppliers ( Ci ( t )), and the cost of core company ( CF ( t )) [ 6 ]. In the following part, this paper will analyze the effects of TI, BMD and the interaction between the two on business performance based on the framework proposed by Zott et al.

TI is the process which includes the generation of new ideas, R&D, trial production, manufacturing and commercialization. As mentioned above, TI can be classified into ITI and RTI [ 7 ]. ITI improves existing technologies and products to meet customer’s current needs, so as to improve business performance [ 8 , 9 , 10 ]. It reduces the cost of products and improves the quality of existing products by improving existing technology continuously [ 11 ]. When the cost of the product is reduced, on one hand, the enterprise would choose to keep price unchanged ( P ( t ) →), and increase the profit of the enterprise ( VF ( t )↑) because of cost reduction ( CF ( t )↓) [ 12 , 13 ]. On the other hand, it could also choose to reduce the price of the products ( P ( t )↓) to attract price-sensitive consumers, which will increase the purchase volume ( n ( t )↑), thus increasing the total income of the enterprise ( TV ↑) [ 14 , 15 ]. When the quality of product/service is improved, the enterprise can increase the price of the product ( P ( t )↑) [ 16 ]. Reliable product quality will also bring good reputation to the enterprise, attract more consumers, and enlarge sales amount( n ( t )↑) [ 17 ]. Therefore, excellent quality will increase the price and sales amount. In addition, in the supply chain, companies can enhance their bargaining power thanks to ITI, so that the company’s expenditure on suppliers or partners will reduce ( Ci ( t )↓), and the income of the focal firm will increase ( Ri ( t )↑). Thus, ITI has an obviously positive influence on enterprise performance due to P ( t )↑, Ci ( t )↓, Ri ( t )↑, CF ( t )↓ and n ( t )↑ [ 18 ].

RTI targets new potential markets, which enable companies to enter new market successfully or to redefine the existing industries [ 19 , 20 , 21 ]. It provides new products for customers, increases the number of types of transactions ( T ↑) and provides for customers with unique products to increase the switching cost of customers, which creates a lock-in effect, so core companies can charge customers at higher price ( P ( t )↑) (at least initially). When it comes to suppliers or partners, core enterprises can first enter the market as a result of RTI, and gain the right to speak in the supply chain to have a high bargaining power [ 22 , 23 ]. This enables the enterprise to increase income ( Ri ( t )↑) and reduce expenditure ( Ci ( t )↓) in the process of cooperation with suppliers and partners. As a breakthrough innovator, even a new market creator, enterprises may experience limited initial transactions ( n ( t )↓). As the markets have undergone earth-shaking changes nowadays, consumers become more willing to accept RTI [ 24 , 25 , 26 ]. Therefore, RTI has a significantly positive effect on enterprise performance because of P ( t )↑, Ri ( t )↑, Ci ( t )↓, and T ↑.

The scale of the enterprise plays a moderating role in the process of TI affecting enterprise performance. First, enterprises with different sizes have different innovation advantages [ 27 ]. Large-scale enterprises with a sound management system and good R&D capability can ensure that TI develops along the path of reducing costs and improving production processes [ 28 ]. Therefore, as the size of the enterprise increases, the ability to implement ITI is enhanced. While smaller enterprises have strong sense of innovation, flexible organizational structure, smooth internal communication, and rapid response to environmental changes. They often choose to entrust external organizations to develop or implement collaborative R&D for poor innovative resources and weak innovative capabilities. Collaborative innovation with external organizations enables enterprises to have access to a wealth of heterogeneous knowledge which facilitates ITI. Therefore, as the size of the enterprise becomes smaller, the ability to implement RTI is enhanced.

Moreover, both profitability of ITI and RTI are influenced by the size of the enterprises. Large-scale enterprises can expand the benefits of ITI due to scale effect. As mentioned earlier, ITI improves existing technologies, which can reduce the unit cost of products and services and improve the quality of them. In large-scale enterprises, when the sales volume is large ( n ( t )↑+), they can enjoy benefits of lower unit cost ( CF ( t )↓+), so enterprises can obtain more profit ( VF ( t )↑). Here, the "+" and "−" indicate the "positive" and "negative" moderating effect of enterprise scales. Large-scale enterprises have scale effect and can amplify the impact of price changes ( P ( t )↑+), which brings higher economic returns ( TV ↑) to enterprises owing to scale effect. In the face of suppliers or partners, large-scale enterprises have greater bargaining power and more speaking rights than small companies, so in the process of working with suppliers and partners, bigger companies can get more revenue ( Ri ( t )↑+) and pay less ( Ci ( t )↓+) to suppliers or partners. Because of scale effect, the R&D costs of unit products in large-scale enterprises are lower than SMEs ( CF ( t )↓+). Therefore, as the size of the enterprise becomes larger, the role of ITI in enterprise performance will become more important because of P ( t )↑+, Ri ( t )↑+, Ci ( t )↓+, CF ( t )↓+, n ( t )↑+.

Smaller enterprises can hardly compete with large-scale enterprises in the mainstream market due to their limited market development capabilities. Small enterprises often choose niche markets to find something to grip. In order to meet the demands of fringe market customers, they need to adopt new RTI to provide new products and services. RTI takes advantage of the knowledge that is far from the company's existing knowledge base, which results in more uncertainty and higher risk. Senior enterprise leaders focus on the profitability of new technologies, while technology developers focus on the novelty of technology, which lead to poor communication. Smaller-scale enterprises have fewer management levels, smooth communication, and even the top leaders of the company are directly responsible for R&D, which can effectively reduce the risk of poor communication between technical personnel and senior management. In large-scale enterprises, there are many organizational levels, and the communication between the upper and lower levels is ineffective. This may lead to RTI, but it may be unprofitable. As the size of the enterprise becomes larger, the risk of RTI increases, which leads to a decline in corporate profit( P ( t )↑−, Ri ( t )↑−, Ci ( t )↓−, T ↑−). Therefore, the scale of the enterprise negatively regulates the role of RTI.

From the above, this paper proposes the following assumptions:

H1a ITI positively affects BP.

H1b RTIs positively affects BP.

H1c The scale of the enterprise positively regulates the influence of ITI on BP.

H1c The scale of the enterprise negatively regulates the influence of RTI on BP.

Business model describes how an organization is associated with external stakeholders and how to trade with them to create value for all stakeholders. From the perspective of value creating, BMD can be divided into two types: efficiency-centered business model design (EBMD) and novelty-centered business model design (NBMD). EBMD creates value by optimizing value chain to improve transaction efficiency and reduce transaction costs. It focuses on improving transaction efficiency, weakening the impact of uncertainties in the environment, and reducing transaction risk and communication and coordination costs for all parties ( CF ( t )↓, Ci ( t )↓), which allows the participants involved in the transaction to obtain higher returns (TV↑). By reducing transaction costs, enterprises can attract more price-sensitive consumers at a lower price, which increases the number of existing customers ( n ( t )↑) and then improves enterprise performance. Therefore, EBMD positively affects enterprise performance because of CF ( t )↓, Ci ( t )↓, and n ( t )↑.

NBMD creates new business models and user experiences, which inspires consumers’ willingness to pay higher price, such as Apple's APP Store. NBMD increases enterprise performance. There are three reasons for this: Firstly, NBMD gives people a refreshing feeling, improves the reputation of the enterprise, expands user markets ( n ( t )↑), and increases the pricing power ( P ( t )↑). Secondly, the focal firm has first-mover advantage, so the stakeholders can generate higher conversion costs which enhance the bargaining power of core enterprises( Ri ( t )↑, Ci ( t )↓). Thirdly, NBMD makes the focal firm reconnect with old and new trading partners in new ways, which will enlarge the sales volume of products or services ( n ( t )↑). Therefore, NBMD positively affects enterprise performance due to P ( t )↑, Ri ( t )↑, Ci ( t )↓, and n ( t )↑.

The effect of BMD on enterprise performance is moderated by enterprise scale. Large-scale enterprises have strong market capabilities, sound trading mechanisms, and strong bargaining power, which can gain more information on products and services to reduce information asymmetry with their own advantages. This provides necessary conditions for enterprises to successfully implement EBMD and is conducive for them to adopt EBMD. Therefore, the effect of EBMD is strengthened in large-scale enterprises ( CF ( t )↓+, Ci ( t )↓+, and n ( t )↑+).

SMEs have high innovation passion and flexible organizational structure, which facilitates smaller firms to trade with partners in new ways. For example, in the anti-virus software market, when Kingsoft and Kaspersky provided paid anti-virus software, Qihoo 360, a provider of Internet and mobile security products and services, provided anti-virus software for free, which made Qihoo 360 gain a large number of customers and grew rapidly. On the contrary, Because of organizational inertia, medium-sized enterprises have greater resistance to implementing novel business models compared with small and micro-enterprises. The effect of NBMD in medium-sized enterprises has been weakened( P ( t )↑−, Ri ( t )↑−, Ci ( t )↓−, n ( t )↑−).

In summary, this paper proposes the following assumptions:

H2a EBMD affects BP positively.

H2b NBMD affects BP positively.

H2c The scale of the enterprise moderates the influence of EBMD on BP positively.

H2d The scale of the enterprise moderates the influence of NBMD on BP negatively.

The premise that innovators can earn profit from TI is the successful commercialization of TI according to the innovation profit theory. TI and BMD strengthen each other, because they have the same goal. Both of them create value for customers. TI and business model in the business ecosystem is reflected in the interaction mechanism of “push–pull.” In addition, the combination of TI and BMD increases the difficulty of imitating and reduces the possibility of competitors’ imitation. Chesbrough et al. stated that business model plays an important role in the process of TI monetization, and business model is a bridge between technology and its economic value. Yao Mingming and other researchers find that the fit between BMD and TI strategy can significantly improve the performance of latecomer enterprises in China in the process of technology catch-up, Therefore, the interaction of TI and BMD is beneficial to improve enterprise performance.

(1) Interaction between ITI and BMD.

As mentioned above, ITI has a remarkably positive effect on business performance due to P ( t )↑, Ci ( t )↓, Ri ( t )↑, CF ( t )↓, and n ( t )↑. It focuses on the customers’ needs of the existing markets and keeps improving the existing technologies, so we can say that ITI is a kind of gentle innovation, which would not have a devastating impact on existing technologies and markets. When ITI and BMD interact with each other, ITI plays a supporting role which would not change the effect of BMD’s leading role.

The efficient business model concentrates on reducing communication coordination costs for all parties of the transaction ( n ( t )↑) by reducing uncertainty in the transaction process and enhancing transaction efficiency, which will improve the performance of core enterprises. When ITI matches with EBMD, the purpose of both is to reduce costs and improve efficiency. They have the same logic and can enhance the promotion of enterprise performance. Therefore, ITI and EBMD positively affect business performance due to P ( t )↑, Ci ( t )↓, Ri ( t )↑, CF ( t )↓, and n ( t )↑. NBMD re-connects old trading partners and new trading partners in new ways. When NBMD matches with ITI, NBMD plays a leading role, and the fit between the two positively affects the performance of the core enterprise due to P ( t )↑, Ri ( t )↑, Ci ( t )↓, n ( t )↑.

Enterprise scale moderates the interactive effects between ITI and BMD on innovation performance. With a sound management system and good R&D capabilities, large-scale enterprises are good at implementing ITI and get more benefits from ITI. In addition, large-scale enterprises have strong market capabilities, sound trading mechanisms, and strong bargaining power, which can reduce the information asymmetry in transaction process that is helpful to carry out EBMD. Therefore, as the size of the enterprise becomes larger, the interaction between ITI and EBMD will be enhanced ( P ( t )↑+, Ci ( t )↓+, Ri ( t )↑+, CF ( t )↓+ and n ( t )↑+). Larger-scale enterprises are not always good at NBMD due to lower innovation spirit and greater inertia. As the size of the enterprise becomes larger, the effect of ITI on the performance of SMEs increases, while the effect of NBMD decreases. Therefore, it is uncertain how the enterprise scale regulates the interaction between ITI and NBMD. This paper proposes the following hypotheses:

H3a The interaction between ITI and EBMD affects BP positively.

H3b The interaction between ITI and NBMD affects BP positively.

H3c The enterprise scale adjusts the interaction between ITI and EBMD positively.

H3d1 The enterprise scale moderates the interaction between ITI and NBMD positively.

H3d2 The enterprise scale moderates the interaction between ITI and NBMD negatively.

It should be noted that H3d1 and H3d2 are the hypotheses that cannot coexist. If neither H3d1 nor H3d2 is supported, it indicates that the size of the enterprise does not have a moderating effect on the interaction between ITI and NBMD.

(2) Interaction between RTI and BMD.

As mentioned above, RTI has a significantly positive effect on business performance as a result of P ( t )↑, Ri ( t )↑, Ci ( t )↓, and T↑. RTI is aimed at new potential markets. RTI usually provides customers with novel products and services that need new technologies far from firms’ knowledge base. It has a greater impact on existing technologies and markets. Therefore, RTI plays an important role in the process of fitting RTI with BMD. EBMD lays emphasis on the efficiency, not the novelty of business model and is a gentle innovation. Therefore, in the combination of RTI and EBMD, RTI plays a leading role. The fit between RTI and EBMD affects business performance positively because of P ( t )↑, Ri ( t )↑, Ci ( t )↓, and T↑.

Unlike EBMD, NBMD connects new partners or old partners in new ways, which is a kind of radical innovation. The combination of RTI and NBMD has greater uncertainty. There are two views in current literature. One view holds that NBMD increases the difficulty of commercialization of RTI. Too much novelty increases uncertainty and may lead to the loss of stakeholders and customers. But Zott and Amit thought that this view was not always correct, because NBMD may contain familiar design elements. The other view holds that the unique combination of RTI and NBMD gives enterprises unique advantages which will create value for them. As mentioned above, NBMD establish high switching costs for stakeholders( P ( t )↑、 Ci ( t )↓、 Ri ( t )↑), which make it easier for stakeholder to accept RTI ( n ( t )↑), and increase transaction type ( T ↑). Rhoads K.'s research showed that enterprises might be limited by resources and capability in the process of RTI, but NBMD could weaken the potential limitations of resources and capacity. Therefore, RTI and NBMD will have a positive joint effect on business performance because P ( t )↑, Ri ( t )↑, Ci ( t )↓, n ( t )↑, T ↑.

As the scale of enterprises increases, they are usually not good at RTI for lack of innovation spirit and technical rigidity. As mentioned previously, small companies are good at implementing NBMD. The scale of the enterprise, therefore, negatively regulates the interaction between RTI and EBMD ( P ( t )↑-, Ri ( t )↑-, Ci ( t )↓-, n ( t )↑-, T ↑-). Large-scale enterprises have strong market capability, sound trading mechanisms, strong bargaining power, and access to more information of products and services, and are experts in EBMD. When the scale of enterprises increases, the ability of RTI would decrease, and the innovation ability of NBMD will be enhanced. Thus, it is uncertain how enterprise size influences the interaction on business performance between RTI and EBMD.

Therefore, this paper proposes the following assumptions:

H4a The interaction between RTI and EBMD affects BP positively.

H4b The interaction between RTI and NBMD affects BP positively.

H4c1 The enterprise scale moderates the interaction between RTI and EBMD positively.

H4c2 The enterprise scale moderates the interaction between RTI and EBMD negatively.

H4d The enterprise scale moderates the interaction between RTI and NBMD negatively.

It should be noted that H4c1 and H4c2 are mutually exclusive. If neither H4c1 nor H4c2 is supported, it indicates that enterprise size does not moderate the interaction between RTI and EBMD. In summary, the role of innovation in business performance and the moderating effect of enterprise size are shown in Table 1 and Fig.  1 . Table 1 illustrates the impact of technological innovation, business model design, and their interaction on the performance of SMEs. Figure  1 shows the theoretical hypothesis model of this article.

Theoretical hypothesis model

In order to test whether the above assumptions are correct, this paper constructs a regression model.

Direct effects model

BP is business performance of SMEs. Y is the age of enterprises . R is R&D intensity . IC1, IC2, IC3, IC4, IC5, IC6 and IC7 are dummy variables , which mean the types of AI industry. There are 8 types represented by 7 dummy variables. ITI is incremental technological innovation . RTI is radical technological innovation . EBMD is efficiency-centered business model design . NBMD is novelty-centered business model design . In order to measure the influence of the age of enterprise and investment in R&D on business performance, this paper takes the age of enterprise ( Y ), R&D intensity ( R ) and industry type ( IC ) as control variables.

Interaction model

In order to test the adjustment effect of enterprise scale, this article divides the whole sample into small and micro-enterprises and medium-sized enterprises. Compare the size of the standardized coefficients in the small and micro-enterprise model and the medium-sized enterprise model to illustrate the moderating effect of enterprise scale.

In small and micro-enterprises, the formulas for direct and indirect effects are as follows:

In medium-sized enterprises, the formulas for direct and indirect effects are as follows:

3.1 Variable measurement

In order to ensure the reliability and validity of the questionnaire, this paper tries to use mature questionnaire and modify it according to the purpose of this study. ITI and RTI are both measured by four items. The measurement items for ITI are “ ① improving existing products and services [ 20 ]; ② enhancing existing production efficiency [ 21 ]; ③ adding extended services to existing customers [ 22 ]; ④ adapting the types of existing products and services [ 23 ].” RTI are measured by “ ① developing new products and services [ 25 ]; ② developing new markets [ 26 ]; ③ opening up new sales channels [ 27 ]; ④ introducing new processes and technologies [ 28 ].” The authors used exploratory factor analysis to optimize the questionnaire, eliminating the items with poor consistency and selecting eight items to measure EBMD and NBMD [ 29 ]. The measurement items of EBMD are “ ① high transaction speed [ 30 ]; ② low inventory costs of suppliers and partners [ 31 ]; ③ simplifying transaction process [ 32 ]; ④ reducing error rate [ 33 ]; ⑤ reducing partner cost [ 34 ]; ⑥ easy access for consumers to gain enterprise information [ 35 ]; ⑦ providing product and service information to participants [ 36 ]; ⑧ making transactions faster and more efficient [ 37 ]." The measurement items of NBMD are ① providing the combination of new products, services and information [ 38 ]; ② attracting new consumers [ 39 ]; ③ providing innovative consuming rewards [ 40 ]; ④ attracting consumers in innovative ways [ 41 ]; ⑤ communicating participants in a creative way [ 42 ]; ⑥ leading role in NBMD [ 43 ]; ⑦ continuously introducing innovation [ 44 ]; ⑧ the innovative business model [ 45 ].” Business performance items include ① customer loyalty [ 46 ]; ② sales growth rate [ 47 ]; ③ profit margin [ 48 , 49 ]; ④ rate of return on investment [ 50 , 51 ].” The answer of questionnaire is designed to utilize five-point Likert Scale [ 52 , 53 ].

3.2 Sample survey

Using a cross-sectional survey method, the date was collected by sending questionnaires to SMEs in the artificial intelligence industry located in the Yangtze Delta Region in China. Using a stratified random sampling method, we selected firms according to the criteria: firms that conduct business model design and technological innovation at the same time. For each firm, we administered the questionnaire to senior managers. In order to improve the reliability and validity of the data,47 firms were selected for a pretest. Finally, a total of 400 questionnaires were distributed via field survey and email, of which 268 valid questionnaires remained, the effective response rate of 67%. The detail of the firms surveyed is shown in Table 2 . According to Small and Medium Sized Enterprise Standardization Regulations issued by Chinese government, small and micro-enterprises usually have a turnover of less than 20 million while medium-sized enterprises more than 2001 million. The sample enterprises include 158 small and micro-enterprises and 110 medium-sized enterprises.

3.3 Common method bias

In order to avoid common method biases, the project team took a series of measures to try to control the possibility of common method bias in the process of questionnaire design and data collection. This paper uses the Harman single factor test to test the common method bias by observing the results of non-rotating factor analysis. The test results show that the contribution rate of the first factor extracted before the rotation is 22.032%, which is lower than the critical value of 40%, indicating that the common method deviation is not significant.

3.4 Reliability and validity analysis

3.4.1 reliability test.

In this paper, the Cronbach α coefficient is used to analyze the reliability of the five main variables in the questionnaire. The results are shown in Table 3 . It can be seen from the table that the Cronbach α coefficients of the five variables all meet the standard requirement of 0.7. This means that the variables show good consistency internally and thus pass the reliability test.

3.4.2 Validity test

(1) Content validity.

The scale in this article is a mature research scale obtained by rigorous bilingual translation. The author draws on the opinions given by experts, so the questionnaire has good content validity.

(2) Convergence validity.

With the help of AMOS 20.0 software, this paper uses the structural equation model to do confirmatory factor analysis on the variables. The results are shown in Table 4 . It can be seen from Table 4 that although the economic performance value of χ 2 /df and the RMSEA value of innovation strategy and economic performance are slightly higher than the standard, all three types of variables have good convergence validity in general.

(3) Discriminant validity.

In this paper, the discriminant validity test is carried out by using the comparison between the square root of AVE and the correlation coefficient between the dimensions. As shown in Table 3 , the square roots of AVE of the five variables are, respectively, 0.805, 0.799, 0.683, 0.774, and 0.775, which are higher than the correlation coefficient between any two variables. This shows that the five variables have good discriminant validity.

4 Results and discussion

Based on the data collected by the questionnaire survey, with the help of SPSS, this paper uses the hierarchical regression method to analyze the effects of TI , BMD and their interaction on business performance. The result is shown in Table 5 .

In Full sample model, M01 shows the direct impact of control variables, TI and BMD on BP. The formula is as follows

M02 shows the impact of the interaction of TI and BMD on BP. The formula is as follows:

It can be seen from the model M01 in Table 5 that the coefficients of ITI , RTI , EBMD and NBMD are all significant at the level of 0.01. It means that both TI and BMD can improve the performance to SMEs. This conclusion is consistent with the research conclusions of Li Yi, Li Jianli, Zhang Wei and others. Both EBMD and NBMD are beneficial to business performance. This conclusion is also consistent with the research conclusions of Yao Mingming, Li Wei and others that BMD positively affects business performance. Therefore, H1a, H1b, H2a, and H2b are confirmed. In model M02, the coefficients of ITI  ×  EBMD、ITI  ×  NBMD、RTI  ×  EBMD and RTI  ×  NBMD all are all significant at the level of 0.001. As shown in Fig.  2 , the interaction between TI and BMD can bring great performance to enterprises for the full sample. The research conclusions of K. Rhoads and others also show that RTI and NBMD are more conducive to improve business performance [ 18 ]. Therefore, H3a, H3b, H4a, and H4b are confirmed. Existing researches reveal that the fit between TI and BMD is helpful to improve business performance, but there is no comparison effect between different types of TI and BMD . It is confirmed that both the fit between ITI and EBMD and the fit between RTI and NBMD can bring greater economic benefits to enterprises in model M02.

Business model design regulates the impact of technological innovation on corporate performance

In Table 5 , M11 demonstrated the direct impact of controlled variables, TI and BMD on the BP of small and micro-enterprises. The formula is as follows:

M12 demonstrated the interaction of TI and BMD on the BP of small and micro-enterprises. The formula is as follows:

M21 shows the direct impact of control variables, TI and BMD on the BP of medium-sized enterprises. The formula is as follows:

M22 shows the impact of the interaction of TI and BMD on the BP of medium-sized companies. The formula is as follows:

Comparing the coefficients of ITI in model M11 and M21, the coefficient of ITI in medium-sized enterprise is higher than the coefficient of ITI in small and micro-enterprise which is not significant at the level of 0.5. This means that as the size of the enterprise becomes larger, the role of ITI in business performance is enhanced. The scale of the enterprise moderates the effect of ITI in business performance positively, so H1c is supported. Similarly, the scale of the enterprise moderates the effect of EBMD on business performance positively, thus H2c is supported. In the M11 and M21 models, the coefficient of RTI in small and micro-enterprises is higher than that in medium-sized enterprises, which indicates that as the size of enterprises becomes larger, the effect of RTI on business performance will decrease. The scale of the enterprise moderates the effect of RTI on business performance negatively, therefore H1d is supported. Similarly, the scale of the enterprise moderates the effect of NBMD on business performance negatively, so H2d is supported. Existing researches show that both ITI and RTI have a promoting effect on business performance, but few of them take the size of the business into account. This study analyzes the moderation of enterprise size which expend the research on the effects of TI on business performance.

TI does not necessarily bring economic benefits to enterprises unless it is matched with BMD . It shows that different types of TI and BMD have different effects on business performance. The effects of the fit between TI and BMD on business performance vary as the size of enterprise changes. However, the existing literature rarely analyzes the moderation of enterprise size in the fit effect of TI and BMD . This paper studies the moderation of enterprise size based on the combination of TI and BMD . In model M12 and M22, the coefficient of ITI  ×  EBMD in medium-sized enterprises is higher than that in small and micro-enterprises, which indicates that as the size of enterprises becomes larger, the interaction between ITI and EBMD will be enhanced. The scale of enterprises moderates the interaction between ITI and NBMD positively, so the results support H3c. Similarly, the scale of enterprises moderates the interaction between ITI and NBMD positively. H3d1 is supported while H3d2 is not supported. Comparing the coefficients of RTI  ×  EBMD between M12 and M22, it can be seen that the coefficient in small and micro-enterprises is higher than that in medium-sized enterprises. It is clearly evident from Fig.  2 that as the size of enterprises becomes larger, the interaction between RTI and EBMD recedes. The size of enterprises moderates the interaction between RTI and EBMD negatively. Therefore, H4c2 is supported and H4c1 is not supported. Similarly, the scale of enterprises negatively regulates the interaction between RTI and NBMD , so H4d is supported.

5 Conclusion

Innovation plays a vital role in the survival and development of SEMs. The role of TI, BMD, and the fit between the two in business performance has been widely recognized by the academic community. Previous researches studied the effects of TI and BMD in promoting business performance without taking enterprise size into account. They examined that the fit between TI and BMD can better promote the development of enterprises. However, few of them compared the fit effects of different types of TI and BMD, and even fewer investigated the change in fit effects of enterprises with different scales. Therefore, this paper collects data of 268 SEMs with questionnaires and uses hierarchical regression analysis to explore the effects of TI, BMD and the fit between the two on business performance. The research results show that different types of TI and BMD and the fit between the two have different effects on business performance of SEMs with different scales, which provides theoretical guidance for SMEs to carry out TI and BMD. The conclusions and implications display as follow:

The direct effects of different TI and BMD on business performance are positive in the whole sample. This suggests that for most enterprises, TI and BMD can bring good performance to them. However, when considering the size of the enterprise, the effect of TI and BMD on business performance is not always significant. In small and micro-enterprises, RTI and NBMD have a greater direct effect on business performance than ITI and EBMD. In medium-sized companies, the opposite is true. Therefore, SMEs should take enterprise size into account when formulating innovation strategy. Small and micro-enterprises should reinforce the novelty of TI and BMD, while medium-sized enterprises should focus on efficiency. In the view of interaction of innovation, in small and micro-enterprises, the coefficient of RTI  ×  BMD is higher than that of the whole sample. Moreover, the coefficient of RTI  ×  NBMD is the highest one. In contrast, in medium-sized companies, the coefficient of interaction between ITI and BMD is higher than that of the full sample. Furthermore, the coefficient of interaction between ITI and EBMD has the highest value. Therefore, enterprises need to take into account the fit between TI and BMD when implementing innovation strategies. And the effects of innovation strategies are moderated by enterprise sizes. Small and micro-enterprises need to pay attention to the fit between RTI and NBMD, while medium-sized enterprises should attach importance to the fit between ITI and EBMD. NBMD reduces the harm caused by RTI and the fit between the two is beneficial to improve the innovation performance of enterprises. This conclusion is supported in small and micro-enterprises, but is not supported in medium-sized enterprises. In small and micro-enterprises, the interaction between RTI and NBMD has the greatest effect on business performance, which shows that the fit between the two can better improves business performance. In medium-sized enterprises, the coefficient of RTI  ×  NBMD is not significant at the level of 0.01. In brief, it depends on the size of the company whether the combination of RTI and NBMD improve business performance. The study enriches the theory of the fit between TI and BMD.

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Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

Small- and medium-sized enterprises

• Technological innovation

Incremental technological innovation

Radical technological innovation

• Business model design

Efficiency-centered business model design

Novelty-centered business model design

• Business performance

H. Chesbrough, R.S. Rosenbloom, The role of the business model in capturing value from innovation: evidence from Xerox Corporation’s technology spin-off companies. Ind. Corp. Change. 11 (3), 529–555 (2002)

Article   Google Scholar  

R. Gatautis, E. Vaiciukynaite, A. Tarute, Impact of business model innovations on SME’s innovativeness and performance. Balt. J. Manag. 14 (4), 521–539 (2019)

H. Guo, J. Tang, Z. Su, J.A. Katz, Opportunity recognition and SME performance: The mediating effect of business model innovation. R&D Manag. 47 (3), 431–442 (2017)

H. Sapra, A. Subramanian, K.V. Subramanian, Corporate governance and innovation: theory and evidence. J. Financ. Quant. Anal. 49 (4), 957–1003 (2014)

C. Baden-Fuller, M.S. Morgan, Business models as models. Long Range Plan. 43 (2–3), 156–171 (2010)

T. Rayna, L. Striukova, From rapid prototyping to home fabrication: how 3D printing is changing business model innovation. Technol. Forecast. Soc. Chang. 102 (3), 214–224 (2016)

C. Zott, R. Amit, L. Massa, The business model: recent developments and future research. J. Manag. 37 (4), 1019–1042 (2011)

Google Scholar  

Y. Taran, H. Boer, P. Lindgren, A business model innovation typology. Decis. Sci. 46 (2), 301–331 (2015)

B.W. Wirtz, A. Pistoia, S. Ullrich, V. Göttel, Business models: origin, development and future research perspectives. Long Range Plan. 49 (1), 36–54 (2016)

C. Jiang, R. Li, T. Chen, C. Xu, L. Li, S. Li, A two-lane mixed traffic flow model with drivers’ intention to change lane based on cellular automata. Int. J. Bio-Inspired Comput. 6 (4), 229–240 (2020)

M. Brettel, S. Strese, T.C. Flatten, Improving the performance of business models with relationship marketing efforts - An entrepreneurial perspective. Eur. Manag. J. 30 (2), 85–98 (2012)

T. Saebi, N.J. Foss, Business models for open innovation: Matching heterogeneous open innovation strategies with business model dimensions. Eur. Manag. J. 33 (3), 201–213 (2015)

C. Zott, R. Amit, Business model design and the performance of entrepreneurial firms. Organ. Sci. 18 (2), 181–199 (2007)

D. Mitchell, C. Coles, The ultimate competitive advantage of continuing business model innovation. J. Bus. Strateg. 24 (5), 445–457 (2003)

B. Demil, X. Lecocq, Business model evolution: in search of dynamic consistency. Long Range Plan. 43 (2), 227–246 (2010)

C. Zott, R. Amit, The fit between product market strategy and business model: implications for firm performance. Strateg. Manag. J. 29 (1), 1–26 (2008)

J.L. Ferreras-Méndez, S. Newell, A. Fernández-Mesa, J. Alegre, Depth and breadth of external knowledge search and performance: the mediating role of absorptive capacity. Ind. Mark. Manage. 47 (5), 86–97 (2015)

Z. Liu, L. Lang, B. Hu, L. Shi, B. Huang, Y. Zhao, Emission reduction decision of agricultural supply chain considering carbon tax and investment cooperation. J. Clean. Prod. 294 (4), 126305 (2021)

Z. He, P. Wong, Exploration vs. Exploitation: an empirical test of the ambidexterity hypothesis. Org. Sci. 15 (4), 481–494 (2004)

J. Zhu, Q. Shi, P. Wu, Z. Sheng, X. Wang, Complexity analysis of prefabrication contractors’ dynamic price competition in mega projects with different competition strategies. Complexity (2018). https://doi.org/10.1155/2018/5928235

J. Sun, X. Lv, Feeling dark, seeing dark: mind–body in dark tourism. Ann. Tourism Res. 86 (1), 103087 (2021)

X. Lv, Y. Liu, J. Luo, Y. Liu, C. Li, Does a cute artificial intelligence assistant soften the blow? The impact of cuteness on customer tolerance of assistant service failure. Ann. Tourism Res. 87 (3), 103114 (2021)

X. Hu, H. Chong, X. Wang, Sustainability perceptions of off-site manufacturing stakeholders in Australia. J. Clean. Prod. 227 (8), 346–354 (2019)

X. Lv, N. Li, X. Xu, Y. Yang, Understanding the emergence and development of online travel agencies: a dynamic evaluation and simulation approach. Internet Res. 30 (6), 1783–1810 (2020)

S. Liu, F.T.S. Chan, W. Ran, Decision making for the selection of cloud vendor: An improved approach under group decision-making with integrated weights and objective/subjective attributes. Expert Syst. Appl. 55 (8), 37–47 (2016)

W. Ran, S. Liu, Z. Zhang, A polling-based dynamic order-picking system considering priority orders. Complexity (2020). https://doi.org/10.1155/2020/4595316

Article   MATH   Google Scholar  

J. Zhu, X. Wang, P. Wang, Z. Wu, M.J. Kim, Integration of BIM and GIS: Geometry from IFC to shapefile using open-source technology. Autom. Constr. 102 (6), 105–119 (2019)

J. Yang, S. Li, Z. Wang, H. Dong, J. Wang, S. Tang, Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials. 13 (24), 5755 (2020)

J. Sun, X. Wang, N. Xiong, J. Shao, Learning sparse representation with variational auto-encoder for anomaly detection. IEEE Access. 6 (6), 33353–33361 (2018)

B. Yi, X. Shen, H. Liu, Z. Zhang, W. Zhang, S. Liu, N. Xiong, Deep matrix factorization with implicit feedback embedding for recommendation system. IEEE Trans. Ind. Inf. 15 (8), 4591–4601 (2019)

B. Lin, F. Zhu, J. Zhang, J. Chen, X. Chen, N. Xiong, J.L. Mauri, A time-driven data placement strategy for a scientific workflow combining edge computing and cloud computing. IEEE Trans. Ind. Inf. 15 (7), 4254–4265 (2019)

H. Li, J. Liu, R.W. Liu, N. Xiong, K. Wu, T. Kim, A dimensionality reduction-based multi-step clustering method for robust vessel trajectory analysis. Sensors 17 (8), 1792 (2017)

W. Fang, X. Yao, X. Zhao, J. Yin, N. Xiong, A stochastic control approach to maximize profit on service provisioning for mobile cloudlet platforms. IEEE Trans Syst, Man Cybern. Syst. 48 (4), 522–534 (2016)

Y. Chen, W. Zheng, W. Li, Y. Huang, Large group activity security risk assessment and risk early warning based on random forest algorithm. Pattern Recogn. Lett. 144 (4), 1–5 (2021)

Y. Zhou, L. Tian, C. Zhu, X. Jin, Y. Sun, Video coding optimization for virtual reality 360-degree source. IEEE J. Select. Top. Signal Process. 14 (1), 118–129 (2020)

J. Zhao, J. Liu, J. Jiang, F. Gao, Efficient deployment with geometric analysis for mm wave UAV communications. IEEE Wireless Commun. Lett. 9 (7), 1115–1119 (2020)

Z. Xiong, N. Xiao, F. Xu, X. Zhang, Q. Xu, K. Zhang, C. Ye, An equivalent exchange based data forwarding incentive scheme for socially aware networks. J. Signal Process. Syst. 93 (11), 1–15 (2020)

Q. Jiang, F. Shao, W. Lin, K. Gu, G. Jiang, H. Sun, Optimizing multistage discriminative dictionaries for blind image quality assessment. IEEE Trans. Multim. 20 (8), 2035–2048 (2018)

B. Li, Y. Liu, A. Zhang, W. Wang, S. Wan, A survey on blocking technology of entity resolution. J. Comput. Sci. Technol. 4 (35), 769–793 (2020)

J. Hu, H. Zhang, L. Liu, X. Zhu, C. Zhao, Q. Pan, Convergent multiagent formation control with collision avoidance. IEEE Trans. Rob. 36 (6), 1805–1818 (2020)

J. Hu, H. Zhang, Z. Li, C. Zhao, Z. Xu, Q. Pan, Object traversing by monocular UAV in outdoor environment. Asian Journal of Control. (2020). https://doi.org/10.1002/asjc.2415

J. Hu, B. Zheng, C. Wang, C. Zhao, X. Hou, Q. Pan, Z. Xu, A survey on multi-sensor fusion based obstacle detection for intelligent ground vehicles in off-road environments. Front. Inform. Technol. Electron. Eng. 21 (5), 675–692 (2020)

J. Hu, M. Wang, C. Zhao, Q. Pan, C. Du, Formation control and collision avoidance for multi-UAV systems based on Voronoi partition. Sci. China Technol. Sci. 63 (1), 65–72 (2020)

Z. Niu, B. Zhang, J. Wang, K. Liu, Z. Chen, K. Yang, Z. Zhou, Y. Fan, Y. Zhang, D. Ji, Y. Feng, Y. Liu, The research on 220GHz multicarrier high-speed communication system. China Commun. 17 (3), 131–139 (2020)

B. Zhang, Z. Niu, J. Wang, D. Ji, T. Zhou, Y. Liu, Y. Feng, Y. Hu, J. Zhang, Y. Fan, Four-hundred gigahertz broadband multi-branch waveguide coupler. IET Microwaves Antennas Propag. 14 (11), 1175–1179 (2020)

B. Zhang, D. Ji, D. Fang, S. Liang, Y. Fan, X. Chen, A novel 220-GHz GaN diode on-chip tripler with high driven power. IEEE Electron Device Lett. 40 (5), 780–783 (2019)

Y. Liu, B. Zhang, Y. Feng, X. Lv, D. Ji, Z. Niu, Y. Yang, X. Zhao, Y. Fan, Development of 340-GHz transceiver front end based on GaAs monolithic integration technology for THz active imaging array. Appl. Sci. 10 (21), 7924 (2020)

H. Ma, G. Yang, Adaptive Fault tolerant control of cooperative heterogeneous systems with actuator faults and unreliable interconnections. IEEE Trans. Autom. Control 61 (11), 3240–3255 (2016)

Article   MathSciNet   MATH   Google Scholar  

H. Ma, L. Xu, Decentralized adaptive fault-tolerant control for a class of strong interconnected nonlinear systems via graph theory. IEEE Trans. Autom. Control (2020). https://doi.org/10.1109/TAC.2020.3014292

J. Ma, L.X. Xu, G.H. Yang, Multiple environment integral reinforcement learning-based fault-tolerant control for affine nonlinear systems. IEEE Trans. Cybern. 51 (4), 1913–1928 (2021)

X. Zhang, R. Jing, Z. Li, Z. Li, X. Chen, C.Y. Su, Adaptive pseudo inverse control for a class of nonlinear asymmetric and saturated nonlinear hysteretic systems. IEEE/CAA J. Automatica Sinica 8 (4), 916–928 (2020)

A. Li, D. Spano, J. Krivochiza, S. Domouchtsidis, C.G. Tsinos, C. Masouros, S. Chatzinotas, Y. Li, B. Vucetic, B. Ottersten, A tutorial on interference exploitation via symbol-level precoding: overview, state-of-the-art and future directions. IEEE Commun. Surv. Tutorials. 22 (2), 796–839 (2020)

S. Wang, X. Wang, F. Meng, R. Yang, Y. Zhao, Investor behaviour monitoring based on deep learning. Behav. Inform. Technol. (2020). https://doi.org/10.1080/0144929X.2020.1717627

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This work is partially funded by Jiangsu Social Science Fund Project (19TQD006) and Jiangsu University Philosophy and Social Science Research Project (2019SJA0822).

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Sunping Qu performed methodology, data curation, formal analysis and supervision. Hongwei Shi, Lin Yu, Huanhuan Zhao and Yunbo Yu performed investigation, writing—review & editing. Sunping Qu, Hongwei Shi, Lin Yu, Huanhuan Zhao and Yunbo Yu performed writing—original draft. All authors read and approved the final manuscript.

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Qu, S., Shi, H., Zhao, H. et al. Research on enterprise business model and technology innovation based on artificial intelligence. J Wireless Com Network 2021 , 145 (2021). https://doi.org/10.1186/s13638-021-02025-y

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The green IT revolution: A blueprint for CIOs to combat climate change

Companies and governments looking to combat climate change are turning to tech for help. AI, new technologies, and some promising tech-driven business models have raised hopes for dramatic progress.

About the authors

This article is a collaborative effort by Gerrit Becker, Luca Bennici, Anamika Bhargava, Andrea Del Miglio , Jeffrey Lewis , and Pankaj Sachdeva, representing views from McKinsey Technology.

While many organizations’ climate goals are lofty, enterprise technology leaders—CIOs, chief digital innovation officers (CDIOs), and chief technology officers (CTOs), among others—have not always succeeded at turning climate ambitions into reality. One of the biggest reasons is that hard facts and clear paths of action are scarce. Misconceptions and misinformation have clouded the picture of what CIOs and tech leaders should do.

We have done extensive analysis of where technology can have the biggest impact on reducing emissions. To start, we divided technology’s role into two primary types of activities:

• offense—the use of technology and analytics to cut emissions by reducing (improving operational efficiency), replacing (shifting emission-generating activities to cleaner alternatives), and reusing (recycling material)
• defense—the actions IT can take to reduce emissions from the enterprise’s technology estate

Scope of the McKinsey analysis

McKinsey’s emissions analysis for this report focuses on enterprise technology emissions, which are the business IT emissions from the hardware, software, IT services, enterprise communications equipment, mobile devices, fixed and mobile network services, and internal technology teams that a company uses for its own operations and that a CIO has control over. These include the emissions related to the full life cycles of the products and services that an enterprise IT function uses, including their development, delivery, usage, and end of life (exhibit). Our internal services emissions' analysis assumes around 40 percent of IT workers are working from home.

The analysis does not include the emissions from the technology products and services that a company is selling (such as data center capacity sold by hyperscalers), operational technology devices (such as sensors and point-of-sale systems), and cryptocurrency mining.

The defense activities are where the CIO, as the head of IT, can act independently and quickly. This article focuses on defense, specifically the IT elements over which a CIO has direct control. We examined emissions from use of electricity for owned enterprise IT operations, such as the running of on-premises data centers and devices (classified as scope 2 by the Greenhouse Gas Protocol 1 Greenhouse Gas Protocol: Technical Guidance for Calculating Scope 3 Emissions: Supplement to the Corporate Value Chain (Scope 3) Accounting & Reporting Standard , World Resources Institute & World Business Council for Sustainable Development, 2013. Scope 1 emissions are direct emissions from the activities of an organization or under their control, including fuel combustion on site such as gas boilers, fleet vehicles, and air-conditioning leaks; scope 2 emissions are from electricity purchased and used by the organization; and scope 3 emissions are all indirect emissions not included in scope 2 that occur in the value chain of the reporting company, including both upstream and downstream emissions. ), and indirect emissions from technology devices that the CIO buys and disposes of (scope 3). 2 These calculations do not include emissions from technology-driven services sold, such as cloud capacity. (See sidebar, “Scope of the McKinsey analysis.”)

What the facts say

Our analysis has uncovered several facts that contravene some commonly held views about enterprise technology emissions. These facts involve the significant amount of tech-related emissions, the share of emissions from end-user devices, the variety of mitigation options available, and the favorable impact of shifting to cloud computing.

Enterprise technology generates significant emissions

Enterprise technology is responsible for emitting about 350 to 400 megatons of carbon dioxide equivalent gases (CO 2 e), accounting for about 1 percent of total global greenhouse gas (GHG) emissions. At first blush, this might not seem like a lot, but it equals about half of the emissions from aviation or shipping and is the equivalent of the total carbon emitted by the United Kingdom.

The industry sector that contributes the largest share of technology-related scope 2 and scope 3 GHG emissions is communications, media, and services (Exhibit 1). Enterprise technology’s contribution to total emissions is especially high for insurance (45 percent of total scope 2 emissions) and for banking and investment services (36 percent).

This amount of carbon dioxide and equivalent gases is a significant prize for companies under increasing pressure to cut emissions. Progress on climate change requires action on many fronts, and enterprise technology offers an important option that CIOs and companies can act on quickly.

You’re invited

To a McKinsey Technology webinar on the critical role of technology in building a sustainable enterprise on October 25, 9:30–10:30am ET.

The biggest carbon culprit is end-user devices, not on-premises data centers

End-user devices—laptops, tablets, smartphones, and printers—generate 1.5 to 2.0 times more carbon globally than data centers (Exhibit 2). 3 On-premises and co-located data centers used by enterprises, not including data center capacity sold by hyperscalers. One reason is that companies have significantly more end-user devices than servers in on-premises data centers. In addition, the devices typically are replaced much more often: smartphones have an average refresh cycle of two years, laptops four years, and printers five years. On average, servers are replaced every five years, though 19 percent of organizations wait longer. 4 Rhona Ascierto and Andy Lawrence, Uptime Institute global data center survey 2020 , Uptime Institute, July 2020.

More worrisome, emissions from end-user devices are on track to increase at a CAGR of 12.8 percent per year. 5 End-user computing market: Growth, trends, COVID-19 impact, and forecasts (2022–2027) , Mordor Intelligence, January 2022. Efforts to address this could target the major causes of emissions from these devices. About three-fourths of the emissions comes from manufacturing, upstream transportation, and disposal. A significant source of these emissions is the semiconductors that power the devices.

Plenty of low-cost/high-impact options exist, starting with improved sourcing

We have found that when it comes to going green, many CIOs think in terms of investments needed to replace items or upgrade facilities. Our analysis, however, finds that CIOs can capture significant carbon benefits without making a significant investment—and in some cases can even save money (Exhibit 3).

Overall, for example, 50 to 60 percent of emissions related to end-user devices can be addressed through sourcing changes, primarily by procuring fewer devices per person and extending the life cycle of each device through recycling. These options will not require any investment and will lower costs, though companies may want to evaluate the impact on employee experience.

In addition, companies can more aggressively recycle their devices; 89 percent of organizations recycle less than 10 percent of their hardware overall. 6 Sustainable IT: Why it’s time for a green revolution for your organization’s IT , Capgemini Research Institute, 2021. CIOs can put pressure on suppliers to use greener devices, especially as companies in the semiconductor sector are already increasing their commitments to emission reduction. Further low-cost, high-impact actions include optimizing business travel and data center computing needs, as well as increasing the use of cloud to manage workloads.

Moving to cloud has more impact than optimizing data centers

Optimizing an on-premises data center’s power usage effectiveness (PUE) 7 PUE describes how efficiently a computer data center uses energy, expressed as the ratio of total facility energy to IT equipment energy. is expensive and results in limited carbon abatement. If a company were to double what it spends on infrastructure and cloud to reduce PUE, it would cut carbon emissions by only 15 to 20 percent. Structural improvements in data centers and optimized layout can help, but the impact is limited, and many companies have already implemented them. More aggressive measures, such as moving data centers to cooler locations or investing in new cooling tech, are prohibitively expensive.

A more effective approach is to migrate workloads to the cloud. Hyperscalers (also known as cloud service providers) and co-locators are investing significantly to become greener through measures such as buying green energy themselves and investing in ultra-efficient data centers with a PUE equal to or less than 1.10, compared with the average PUE of 1.57 for an on-premises data center. 8 “Uptime Institute 11th annual Global Data Center Survey shows sustainability, outage, and efficiency challenges amid capacity growth,” Uptime Institute, September 14, 2021. (We estimate that companies could achieve just a 1.3 PUE score for their data center if they invested nearly 250 percent more, on average, over what they currently spend for their data centers and cloud presence.)

With thoughtful migration to and optimized usage of the cloud, companies could reduce the carbon emissions from their data centers by more than 55 percent—about 40 megatons of CO 2 e worldwide, the equivalent of the total carbon emissions from Switzerland.

Three steps to take now

With companies and governments under intensifying pressure to cut carbon emissions and with technology playing a key role in delivering on those goals, CIOs will find themselves on the front lines. The challenge will be to reduce IT’s carbon footprint while delivering high-quality, low-cost technology services to customers and employees.

On average, completion of the defensive steps might take three to four years. However, CIOs who act decisively and precisely can achieve 15 to 20 percent of carbon reduction potential in the first year with minimal investment.

CIOs can choose from among a wide array responses, particularly in conjunction with the CEO and the board. However, three measures they can take right now will prepare the organization for longer-term efforts. These measures involve sourcing strategies, key metrics, and a performance management system.

The net-zero transition: What it would cost, what it could bring

Move now on sourcing strategies.

Far and away the fastest and most effective defensive measure for reducing IT carbon emissions is to revise policies for technology sourcing. Optimizing the number of devices in line with standards followed by companies in the top quartile 9 Top quartile in terms of the ratio of devices to people is derived from the number of devices per person. Our analysis uses McKinsey Digital’s Ignite solutions and 2020 data. would reduce about 30 percent of end-user-device emissions, the amount of carbon emitted by Hong Kong. For example, top-quartile companies have one printer for every 16 people in the workplace; the overall average is one printer per eight people.

This sourcing shift does not necessarily lead to a degradation in user experience, because the rollout of 5G and increasingly advanced processing and compute power allow the main processing function to happen at the server. Therefore, devices can be less powerful and consume much less energy. Essentially, this is a software-as-a-service (SaaS) model where high-end and user-friendly experiences happen on the server, not the device. The effectiveness of this approach will depend on having stable networks, less resource-intensive coding at the device level, edge computing capabilities, and shifts of offerings to more efficient platforms (for example, cloud).

As part of this effort, the CIO and the business’s head of procurement will need to collaborate on reviewing and adjusting device refresh timelines and device-to-person ratios, as well as adjusting the basis for purchasing decisions. Procurement generally relies on cost/benefit calculations, and rightly so. That approach will need to expand to account for carbon dioxide emissions. The spirit of collaboration should extend to suppliers as well, with the parties working together to formulate plans that provide the greatest benefits for all.

A more thoughtful sourcing strategy extends beyond end-user devices. CIOs, for example, should look for green sources of the electricity IT uses. When these sources are unavailable, CIOs can direct procurement to power purchase agreements to offset carbon use. CIOs can also set green standards for their vendors and suppliers, requiring GHG emissions disclosures and incorporating them into their criteria for purchase decisions.

Establish a green ROI metric for technology costs

Any real progress on green technology can happen only when companies measure their “green returns.” But today, most green metrics omit cost and savings, which ultimately makes them impractical. A better metric focuses on cost per ton of carbon saved (accounting for costs saved as well). Sophisticated models calculate emissions throughout the full life cycle, including production, transportation, and disposal.

CIOs can further assess suppliers, manufacturers, and service providers based on how advanced they are in recycling and refurbishing electronics; designing circular components; extending product life cycles with better design, higher-quality manufacturing, and more robust materials; offering repair services; and reselling to consumers.

Decisions about IT spending need to consider a range of factors, including technical debt abatement and business strategy. Along with these factors, companies should institutionalize a green ROI metric that is transparent to everybody in the business as an element in IT decision making, including in requests for proposals (RFPs). Doing so will enable companies to better understand the true impact their technology is having on carbon emissions.

Put in place green measurement systems

Establishing a green ROI metric is only a start. CIOs need to establish a baseline of performance, measure progress against the baseline, and track impact in near real time, much as companies track real-time computer and network usage for applications in the cloud. This kind of measuring system ensures that CIOs know what’s working and what isn’t, so they can adjust quickly.

In practice, implementing green measurement can be challenging. Some companies have spent a year measuring their carbon footprint, ending up with an outdated analysis. This tends to happen when companies are determined to measure every bit of carbon emitted, a praiseworthy but time-consuming effort. CIOs can make substantial progress by instead prioritizing measurement where the impact is highest, such as tracking the number of end-user devices purchased and in use, the current duration of use for each device, and the ratio of devices per user. Another way CIOs can make quick progress is to embed emissions- and power-monitoring capabilities into large technology assets and work with external providers, such as electricity companies, to track usage in real time.

Effectively combating climate change won’t happen through one or two big wins; those don’t exist yet. To have real impact, companies and governments will need to act in many areas. Technology has a huge role to play in many of these areas, but CIOs and tech leaders need to act quickly and decisively.

This article is the first in a series about how CIOs can reduce emissions. The next article will explore how CIOs can drive the business’s sustainability agenda by playing offense and implementing reduce, replace, and reuse levers to decarbonize.

Gerrit Becker is an associate partner in McKinsey’s Frankfurt office, Luca Bennici is an associate partner in the Dubai office, Anamika Bhargava is a consultant in the Toronto office, Andrea Del Miglio is a senior partner in the Milan office, Jeffrey Lewis is a senior partner in the New Jersey office, and Pankaj Sachdeva is a partner in the Philadelphia office.

The authors wish to thank Bernardo Betley, Arjita Bhan, Raghuvar Choppakatla, Sebastian Hoffmann, Abdelrahman Mahfouz, Tom Pütz, Jürgen Sailer, Tim Vroman, Alice Yu, and Gisella Zapata for their contributions to this article.

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