Corporate entrepreneurship and ambidexterity - Insights from an in-depth case study
Ecole Polytechnique, France
Because large firms face an increasing competition and a growing need for digital transformation, they tend to innovate repeatedly. Innovation can take many forms and requires firms to develop simulnateously exploration and exploitation capabilities. In that vein, Corporate Entrepreneurship (CE) appears as a solution for firms to develop their ambidexterity.
Indeed, CE allows firms to ensure exploitation of current businesses while exploring opportunities to renew competitive advantages and accelerate their time to market.The existing literature on CE, mainly focuses on three lines of research. The first one addresses the individual characteristics of corporate entrepreneurs (profile, psychology, ambitions etc.) (Antoncic and Hisrich, 2003; Thornberry, 2003; Mohedano-Suanes and Benitez, 2018). A second stream of research on CE focuses on organizational and managerial capabilities. It stresses that the capability of taking advantage of support for CE may vary by managerial level (Hornsby et al, 1993). Also, Kelley et al. (2006) highlight the importance of capabilities needed for forming and managing networks that facilitate non-routine activities. Finally, the third line of research describes different types of CE processes implemented by firms and underlines their respective strengths and weaknesses (Bouchard, 2007; Bouchard and Fayolle, 2011; Phan et al., 2009)
However, despite a growing interest for CE, only a limited attention has been paid to CE programs as drivers of ambidexterity.
Thus we aim at answering the following research questions: (1) To what extent does CE contribute to the ambidexterity of a company? (2) Under which conditions does CE support the development of innovations (either disruptive or continuous)?
To answer these research questions, we rely on a qualitative research design with an embedded multiple case study (Eisenhardt, 1989; Yin, 2012; Dumez, 2016). We studied a large firm that has implemented different CE projects that have generated (or not) innovation outcomes. Air France appears as an ideal case. Air France is one of the leaders of the airline industry. In this sector, competition is fierce and airlines need more and more to digitalize their activity. In 2017, the airline created a novel entity dedicating to driving transformation across the company as regard to innovation processes especially related to digitalization.
In 2018, this new entity launched a CE program to develop new innovative products and services for customers. Once the projects are selected, the CE program supports the teams for one year which is divided into three phases with assigned objectives for each phase. In 2018, five projects were selected to be developed within the CE program. We had the opportunity to study these projects along their development. To our knowledge, this is the first time a longitudinal research design is applied to CE and allows us to highlight the conditions leading to successful CE projects. We collected two main data sets covering the one year development of each CE projects (2018). First, as primary data, we conducted open semi-structured interviews with corporate entrepreneurs who has led the five CE projects, and with various CE program stakeholders. We also collected video records of CE project pitches and discussion between jury members, and various summaries of meetings about the decision-making process of the CE program itself and these five projects. As secondary data, we collected various follow-up project presentations and internal corporate files. Thus this heterogeneous data nature and source allowed us to conduct triangulation and strengthen our analysis.
We first show that (1) Air France’s CE program aims to identify, explore and accelerate time to market of new business opportunities thanks to corporate entrepreneurs. In addition, we identify determining factors under which CE allows ambidexterity. We show that (2) Innovation development and scale-up depend on the CE project nature and its integration into the current firm’s business model. Furthermore, we highlight that (3) to be converted into a prototype and implemented within the firm, not only CE projects has to be held and supported by corporate entrepreneurs but also by top managers. We then discuss (4) the limits of CE in terms of organizational and individual ambidexterity according to CE project nature.
Contribution to Scholarship
Our findings extend previous research on CE such as the meta-analysis conducted by Phan et al (2009) that highlights the determining factors supposed to design corporate entrepreneurship strategy: the structural and procedural contingencies, the management involvement and organizational capacities. Indeed, our article is the first one to investigate corporate entrepreneurship at the micro project level as well as each innovation outcomes.
Contribution to Practice
Also, this analysis allows to go deeper into the understanding of the organizational decision-making process of each project development and implementation and may help managers to understand innovation management according to corporate project nature.
Firms capabilities to articulate both exploration and exploitation activities tends to be a requirement for large firms as well as digitalization is. In that vein, practises like Corporate Entrepreneurship are more and more implemented within large firms to create knowledge.
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Managing of IoT wireless network technologies: case of Russia
1National Research University Higher School of Economics, Russian Federation; 2The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, Russian Federation
IoT technologies can significantly affect the interaction of economic agents. As a result of the growth in the number of connected devices it became possible to combine equipment, information systems and management systems into a single communication network. The basis for the development of IoT is IoT wireless networks.
There are several research gaps in the area under consideration. In some models, including the model of trust dynamics in IoT (Fernandez-Gago et al., 2017), IoT is evaluated from a position of trust, paying insufficient attention to the technological aspects of its development, including assessing the effectiveness of the information infrastructure. On the contrary, Vinob chander et al. (2017) created an application interoperability model for heterogeneous IoT environments in which the technical aspects of IoT are included but without paying attention to other aspects that affect IoT dissemination, including its legal framework. There is a lack of comprehensive studies covering the wide range of IoT wireless networks. Mekki et al. (2018) developed an approach to analyzing LPWAN technologies, highlighting the following factors: frequency, bandwidth, etc. In this research, a number of criteria have been developed which can be used to make a decision on the implementation of LPWAN technologies.
As the technologies of IoT are relatively new and developing dynamically, there is a lack of research in the field of modeling of technology assessment of application potential of IoT wireless network technologies.
The goal is to apply a model for the assessment of IoT wireless network technologies.
1. What are weights of the perspectives and criteria of the model?
2. What are the requirements for the further development of the model?
3. What are the most prospective technologies according to the model?
Methodological foundations of the model are based on four perspectives which are important for telecommunication companies in the implementation of IoT technologies: economic (economies of scale, market expansion, network deployment cost and cost of maintenance), organizational (staff competencies and the ability to conduct research), technological (data transmission range, data rate, energy efficiency, interoperability with other networks, network bandwidth, connection establishment time, radio-frequency penetration, and network scalability) and legal (the degree of elaboration of standards for different IoT wireless network technologies and the necessity to use licensed radio frequencies).
The development of the decision model of assessment of IoT wireless network technologies for telecommunication sector was carried out in 5 stages: desk research, expert validation, expert assessment, decision model calculation, interpretation of model calculation results.
Expert validation and assessment of the criteria were made in a workshop from two departments of The Bonch-Bruevich Saint-Petersburg State University of Telecommunications: Faculty of Radio Technologies of Communication and the Faculty of Info-communication Networks and SystemsThe following parameters were taken into account when selecting candidates: higher technical or economic education, professional experience in research, participation in conferences, and publications in the field of information and communication technologies. For this, a questionnaire was developed, Experts in the workshop were asked to evaluate the attractiveness of a technology for IoT. As part of the discussion, in addition to the proposed list of 10 technologies identified by desk research (RFID, NFC, Bluetooth, WBAN, Zigbee, WPAN, LPWAN, VSAT, 4G, 5G), the experts decided to include Wi-Fi as having potential for IoT. . On the basis of the validation, criteria and the list of technologies, a decision model for the implementation of IoT wireless networks was developed.
On the basis of the expert survey, the most significant perspective is technological, affecting the prospects of implementing a particular IoT wireless network technology. Among the technological perspective, the most essential criteria that affect the application potential of the particular IoT wireless networks technologies are energy efficiency, radiofrequency penetration, data transmission range and data rate. To a lesser extent, organizational perspective such as development of staff competitiveness and research staff availability affect the potential of IoT wireless network technologies. The best result in the framework of the proposed model was shown by LPWAN technology. Concerning the advantages of LPWAN, it is necessary to allocate a moderate range, very modest energy requirements for endpoints of the network, as well as the existence of a whole family of these technologies that imply the possibility of radio access in different frequency bands. The group of LPWAN technologies has an obvious drawback, expressed in the private character of the representatives of this group (proprietary technology) and the poor development of standards at the level of international organizations. However, this feature of the technology family is, rather, a feature of the process of growing the ecosystem of IoT and will be overcome in the future.
Contribution to Scholarship
The study proposed a method for assessing IoT wireless network technologies, which is of interest to the intensification of the digital agenda.In the future, adaptation and calculation of this model is possible not only for Russia, but also for other countries.
Contribution to Practice
The study identified the most and least significant characteristics of technologies within IoT ecosystem. Using the developed technology assessment model and the results of the expert procedures, the authors were able to identify the most significant perspective ― technological one. Within the framework of this perspective, the most significant criteria are energy efficiency, radiofrequency penetration, data transmission range, data rate. Organization perspective is considered a less significant factor but still important for IoT wireless network technologies. A comparison has been made between various technologies and the most relevant ones have been identified for the needs of the Internet of Things.
IoT technologies are one of the fastest growing digital technologies. Methods and approaches for managing these technologies are of particular interest because of the digitalization of the economy and society.
Fernandez-Gago C., Moyano F., Lopez J. (2017) Modelling trust dynamics in the Internet of Things // Information Sciences, 396, 2017, p. 72–82.
Mekki K., Bajic E., Chaxel F., Meyer F. A comparative study of LPWAN technologies for large-scale IoT deployment / ICT Express, 2018. Available at: https://doi.org/10.1016/j.icte.2017.12.005
Vinob chander R., Mukherjee S., Elias S. (2017) An applications interoperability model for heterogeneous Internet of Things environments // Computers & Electrical Engineering, Volume 64, November 2017, p. 163–172.
Populations of R&D in Financial Services IT: A mixed methods study
Grenoble Ecole de Management, Canada
This paper presents a framework to study R&D Projects in Financial Services, by adapting the existing Chatzipetrou, et al., Compositional Data Analysis (CoDa) framework for the study of the assignment of effort on projects by phase / task (Chatzipetrou et al., 2015), combined with a mixed methods case-study.
Because of their macroscopic nature, assimilation, the traditional application of demarcation and synthesis, cannot answer fundamental questions of R&D at the project level. While Howells (2009) describes how services use standard project management approaches to implement innovation, it is unknown if formal R&D management techniques are in use in the service sector for IT projects.
Typically, service sector firms do not setup formal R&D departments (Sundbo, 1997), and conduct R&D through projects or less formal spontaneous approaches (Miles, 2008). Consequently, often managers in the service sector do not recognize the fact they conduct R&D through projects (Miles, 2007). Other researchers have looked at project management from the aspect of effort distribution by phase; for example, how much effort in hours for Initiate, Plan, Design, Build, test and implement. (Yang, He, Li, Wang, & Boehm, 2008).
No known models exist to explain how financial services conducts R&D. Although, we do have a starting point from Miles (2007) who identifies the Frascati accord definitions of R&D in services generally, and specifically for Information Technology. Miles concludes R&D and Project Management can benefit by exchanging ideas.
What are the fundamental differences between R&D and BAU projects in Financial Services , in terms of phase effort allocation, so R&D projects can be better managed with in a non-R&D environment. What is the new knowledge produced (Absorptive capacity) and how does it migrate to BAU projects after its creation?
Quantitatively, a recent paper by Chatzipetrou, et al., suggests a way forward using Compositional Data Analysis (CoDa) which introduces a framework to study the assignment of effort on projects by phase / task (Chatzipetrou et al., 2015). Qualitatively, this Canadian Data-set contains the government issued claim forms for each claim by year, structured by formal narratives for the Technological Objective, Technological Uncertainty (Constraints) and work done in each tax year to overcome them. Combining the CoDa with the case analysis allows for a rich view of when R&D starts, when it is not quite ready and when it is finished
The dataset of 500,000 project time sheet records from a Global Bank’s IT projects from 2004 – 2015. It is for approximately 100,00 projects, for approximately 19 million person hours of which approximately 1.5 million for 655 projects were claimed and audited as R&D, consolidated into 33 claims. It has detailed claim descriptions qualitative data, which describe the multiyear R&D projects, the technological objective, technological uncertainty (constraints) and the hypothesizes and work in each tax year to overcome the constraints (about 5 pages of structured narrative for each year).
The approach is a mixed methods methodology with a case study of the 33 claimed R&D projects to triangulate the case study with the CoDa results. Using Document Analysis methods and CoDa, the research questions are aimed to discover the similarities and differences by effort distribution for are the three populations BAU, R&D and unsuccessful R&D Candidates (candidates were thought to be R&D but did not qualify by internal check/opinion and were never claimed). The management implications are how these R&D projects can be better managed, with recommendations for the application of R&D management practices within the classical project management space.
The two main populations are approximately 99,445 BAU and 655 R&D projects, the latter of which has approximately one third is core R&D work and two thirds supporting work (need to test and validate the core R&D). Approximately 330 projects were identified as Candidates by the management consultants who originally selected the R&D projects from the total population, but after deep scrutiny they could not find them to be valid R&D. Other Candidate projects either occurred the year before or after the end of a valid claim, which means the conditions for eligibility were not yet met or no longer existed. Using the dissimilarity index and CoDa charts, this allows an analysis as to how the allocation of phase effort changes as a project becomes and then ceases to be R&D (if there is a difference). A multilevel document analysis of the project claim descriptions, extracts artifacts of charts that are verbally described, such as Project Milestones, a table of when each Technological Uncertainty was overcome of the reasons why not. The quantitative data is presented for the populations and then individually by major project within the case study, which identifies at the project level new knowledge (Absorptive capacity) created..
Contribution to Scholarship
This thesis presents a framework to study R&D Projects in Financial Services, by adapting the existing Chatzipetrou, et al., Compositional Data Analysis (CoDa) framework for the study of the assignment of effort on projects by phase / task (Chatzipetrou et al., 2015), combined with a mixed methods case-study. This adaption contributes to both the research and practice of R&D in financial services, by providing approaches to work with large historical corporate datasets originally purposed for R&D claims. This novel approach is necessary, because typical approaches using key success factors to study IT projects, cannot be relied upon for R&D projects. Sectorial studies, Government Surveys and the like only provide totals of R&D by Financial Services firms, not project details or analysis of how populations of R&D differ from the rest of the Business as Usual Projects (BAU).
Contribution to Practice
With a clear distinction between the phase effort required for R&D projects, with repeat build/test cycles, proof on concepts, a number of R&D management techniques/recommendations such as technology visioning, stage gate and so forth are reviewed in relation to how some of the projects in the case study could have been managed differently. The analysis of how absorptive capacity is created during the R&D process, not as a vague concept, but ads a measurable output of a project and reused in other non R&D Projects (transfer). Many more recommendations i.e recognizing when R&D is occurring are included
The case study includes several digital projects, some early technology, 2004, some later 2015. In terms of being able to manage R&D projects to create absorptive capacity, this research provides examples of very large digital projects and the lessons learned for how to manage them in a corporate non-R&D environment.
Chatzipetrou, P., Papatheocharous, E., Angelis, L., & Andreou, A. S. (2015). A multivariate statistical framework for the analysis of software effort phase distribution. Information and Software Technology, 59, 149–169. https://doi.org/10.1016/j.infsof.2014.11.004
Déry, D., & Abran, A. (2005). Investigation of the Effort Data Consistency in the ISBSG Repository. 15th International Workshop on Software Measurement, (January 2005), 123–136.
Howells, J. (2009). Services R & D. JRC Euopean Commision - IPTS Working Papers on Corporate R&D and Innovation, (05).
Miles, I. (2007). Research and development (R&D) beyond manufacturing: the strange case of services R&D. R&D Management, 37(3), 249–268. doi:10.1111/j.1467-9310.2007.00473.x
Miles, I. (2008). Patterns of innovation in service industries. IBM Systems Journal, 47(1), 115–128. doi:10.1147/sj.471.0115
Pawlowsky-Glahn, V., & Egozcue, J. J. (2006). Compositional data and their analysis: an introduction. Geological Society, London, Special Publications, 264(1), 1–10. https://doi.org/10.1144/GSL.SP.2006.264.01.01
Sundbo, J. (1997). Management of Innovation in Services. The Service Industries Journal, 17(3), 432–455. doi:10.1080/02642069700000028
Yang, Y., He, M., Li, M., Wang, Q., & Boehm, B. (2008). Phase Distribution of Software Development Effort. In ESEM ’08 Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement (pp. 61–69).
Understanding Firms AI Maturity: An exploratory study
1Rennes School of Business, France; 2Laval University
Firms’ ability to create and capture value from AI is contingent on its managers’ ability to envision the specific applications of AI and foster the right environment for its deployment. In this context, it is important to identify the factors generating differences across firms in terms of Artificial Intelligence maturity.
Firms embarking into AI appropriation face various challenges. Most previous studies have focused on the challenges of acquiring big data analytics (McAfee et al., 2012; Gupta & George, 2016; Wamba et al., 2017). However, according to Brenahan and Pai-Ling (2017), AI applications require clarity on areas where they are going to be applied, rethinking the existing business processes, and potentially redefining the existing business model. Thus motivated, this study seeks to explore the extent to which companies have perceived these or other challenges that lay ahead: and the extent to which they realize the potential impact on their processes and their business model. We also hope to understand the different categories of maturity in terms of AI appropriation, as reflected in the value creation reported by the firms and the level of the various enablers activated within them, building on the ones identified by Warner and Wäger (2019)
This study will address a relatively unexplored area regarding AI: understanding the gap between the espoused vision of top management, and the benefits seen by middle or operational managers. The study will also bring out alignment on misalignment (if any) between top management and middle management
What are the various configurations of AI maturity in terms of the perceived potential of AI by the firm and in terms of readiness in appropriating AI?
What are the differences in perspective between vision (senior managers) and translation (middle/operational managers)?
We are conducting an exploratory study conducting semi-structure interviews to investigate how managers perceive the potential of Artificial Intelligence and the extend to which firms have process in place to appropriate the new technology.
The interviews are conducted in French and are transcribed and then translated into English using Trint and DeepL (Tools that utilize machine learning), and subsequent checking by a bilingual speaker. The data will be analyzed using a combination of conventional qualitative research tool (NVivo) and text mining using neuro-linguistic programming in R through the use of specific packages.
We are conducting interviews with 30 firms and a total of 60 managers. Half of the managers have senior positions and half have operational positions. The purpose is to measure the difference in terms of perceived benefits of AI within the different level of management. The interviews are semi-directed interviews lasting on average 40 minutes.
The 30 companies are located in France and range from start-up to global companies.
The objective of this research is to measure the gap in perspective between different levels of managers (differences between vision and translation) and different categories of firms regarding the expected benefits of AI.
Based on the different perception and the process in place to deploy AI technology, we will identify 3 or 4 configurations of AI maturity
Contribution to Scholarship
Since the adoption of technology is often described as a key to survival, growth, and competitiveness, the goal of this study is to provide empirical evidence on how firms from different sectors and size are perceiving the potential of the technology for their competitive advantage and how they are getting ready to appropriate AI technology.
This study is significant and timely because there is a serious lack of scholarly empirical research on this topic and it will contribute to filling this gap in the extant literature by providing evidence that can inform appropriation process of AI technology.
Contribution to Practice
From a practical perspective, this study is also significant since the findings can serve as a guide, to help managers identify the potential weaknesses in their firm appropriation process of AI and highlight some initial best practices.
From a policy perspective, the findings of this study can inform policymakers of potential causes of AI divide.
The topic of this research paper somewhat matches the topics of tracks 1 and 3 but also the overarching theme on innovation challenges as our research objectives are to identify some of the challenges firms face in creating and capturing value from AI.
Bresnahan, T. F., Davis, J. P., & Yin, P. L. (2014). Economic value creation in mobile applications. In The changing frontier: Rethinking science and innovation policy (pp. 233-286). University of Chicago Press.
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049-1064.
McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D., & Barton, D. (2012). Big data. The management revolution. Harvard Bus Rev, 90(10), 61-67.
Wanba Samuel, ShahriarAkter, Andrew Edwards Geoffrey Chopin, Denis Gnanzou (2015) How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246
Warner and Wager (2019). Building dynamic capabilities for digital transformation: An
ongoing process of strategic renewal. Long Range Planning (in Press) https://doi.org/10.1016/j.lrp.2018.12.001