**4. Results from two action research in joint industry-academia research projects**

Sub-Section 4.1. specifies the context and the methodological choices made to conduct the two action research projects analyzed in this chapter. Sub-Section 4.2 presents the projects and reports the knowledge processes at work. Combining projects analysis, sub-Section 4.3 outlines a framework of knowledge creation dynamic unveiling knowledge processes interactions and identifies some important factors influencing it.

### **4.1 Context and methodological choices to conduct the research projects**

Considering that action research is a "macro design" and that "an obvious challenge for interactive research is to clarify and strengthen its methodological basis" [25], it is important to explicit the context and the methodological choices for the two projects analyzed in this chapter. It is important to remind that the projects have a double objective: 1) undertaking collaborative projects that fit with our research program and with companies' key strategic issues; 2) deepening our understanding of the knowledge processes dynamic leading to knowledge creation.

The company that gave us the opportunity to launch the two research projects is a global manufacturer with prior experience in joint industry-academia research projects. Since its product and service offerings rely on international supply chains, the company is more and more dependent on multi-tiers networks of suppliers and retailers and the quality of the supply chain (SC) execution is crucial. In this company, SCM is considered as a strategic dynamic capability to be developed to succeed in its volatile, uncertain, complex, and ambiguous (VUCA) business environment.

The company contacted us to develop an industry-academy partnership to benefit from the logistics and SCM knowledge of our research center and to boost

### *The Dynamic of Knowledge Creation in Joint Industry-Academia Research Projects… DOI: http://dx.doi.org/10.5772/intechopen.101985*

collaboratively R&D and research. For both projects, we gave a list of research topics aligned with our research program and considered as "gaps" in the academic literature. The company selected the topics fitting the best with its strategic priorities.

Since the beginning, the overall idea was to develop at least one collaborative research project including a 3-years PhD student participating in action and working under a co-supervision. As the academic supervisor, I was expected to be an active member of the research team, participating in the knowledge processes of the project. This was therefore an opportunity to build upon the guidelines from [5], to adopt action research, and to develop "generative learning" [13] to explore, extend and develop results from [5], especially to better understand knowledge processes dynamic leading to knowledge creation.

The collaboration began in early 2018 and the first project (P1) was officially launched in November 2018. The second project (P2) was discussed in early 2020 and was officially launched in October 2020. P1 and P2 have a similar ambition (share and generate knowledge, improve SC performance, develop competences and capabilities) but there are differences in terms of topic, conceptual basis, SC scope, research planning and supervisor in the company (both being logistics and SCM managers with expertise and seniority in the industry and the company).

In both projects, the industry-academia dialogue began at an early stage to refine the research topic, co-construct the project, and clarify the objectives for practice and research. In both projects, a formal 3-years contract agreement (aligned with PhD requirements) has been negotiated, each planning being in three main phases.

At the very beginning of every project, me and the PhD student opened a "research diary" [29] to report on the research process (project traceability). The diary and the research documentation and data produced all along the project are used to develop, in parallel of each project, a meta-level analysis thanks to reflective and reflexive inquiry [21, 27, 30, 31]. Reflexivity mixed with "contemplative activities" [32], in turn, leads me to write a lot of reflective notes about industry-academia interactions, dialogue and co-construction as well as knowledge processes and dynamic in these projects, pointing out problems, questions, ideas, and even emotional reactions. The lessons learned during the beginning of P1 (2018–2019) were reused in P2, and from the moment when the projects overlapped (2020), there are learning interactions between the projects that are not independent.

The action research (AR) choices for both projects combine aspects from the approaches presented in subsection 3.2. The research and practice spheres [23–26] were clearly identified, and partners agree their identity must be preserved (interactive AR), but a collaborative action learning [27] sphere was considered necessary to have more day-to-day dialogue and co-construction (action learning research). The educational task [23] (interactive AR) linked to the participation of a PhD student in each project was crucial to create this collaborative sphere. In P1, the PhD student mainly works in the company and devotes 70% of time in action, in P2, the PhD student mainly works in the research center and devotes 50% of time in action.

Compared to existing literature in supply chain knowledge management research, our methodological choices fill a gap. As stated in [33]: "the low occurrence of the face-to-face mode identifies a significant literature gap for a qualitative topic such as knowledge management in the supply chain". More generally, it fills this gap in the broader logistics and SCM literature that, in the search for more scientific rigor, seems to have "lost its connection with practice" [34].

The overall analysis of the projects, separately and combined, uses the conceptual basis in the highlight boxes in subsections 2.2 and 2.3.

### **4.2 Knowledge processes and their relationships in P1 and P2**

This subsection presents the two action-research joint industry-academia research projects with clear knowledge creation objectives in logistics and SCM. We adopt a narrative form to report industry-academia knowledge processes at work. From the data and in line with the choices made in Section 2, we identified: knowledge transfers from academics to partitioners (coded Ta), knowledge transfers from partitioners to academics (Ti), interactive knowledge sharing (S), research knowledge generation (Gr) – with reference to theory and elaborated during "back-stage-reflections" –, practice knowledge generation (Gp) – related to "on-stage-performance" at the workplace –, and knowledge generation combining both (Gr + p). It is important to note that in action research [21–28] both academics and partitioners can participate to Gr, Gp and Gr + p. Among the many interactions and knowledge processes episodes, we focused on patterns ending with knowledge creation with value for partners and being attested by some "production" (P) (some being public academic publications – work-in-progress papers, conference papers, articles –, others being for internal use in the company and in formats that best suit knowledge transfer and dissemination (in line with ART [28])).

Lessons from P1.

For P1, the overall question is how to improve tracking and tracing systems to develop SC visibility and to create more value to SC stakeholders, the SC scope being the downstream SC. In the beginning of P1, the company clearly wanted to benefit from our 20-years research experience on traceability and tracking/tracing systems. The first knowledge process was (Ta) with a conference about "total traceability", given in the company, starting-up the industry-academia dialogue. The project has been discussed, designed, and written on this prior academic conceptual basis and many knowledge exchanges (Ta + Ti) during the negotiating and contracting period.

In P1, knowledge sharing (S) quickly began "on-stage" with the participation of the PhD student in many R&D projects focused on track and trace issues (diagnosis of existing systems, usefulness of available new technologies, changes in systems and/or in the logistics operations, etc.) and triadic supervision meetings. The first PhD-year included intensive professional and academic learning. (S) about R&D projects raised a question: "why improving track and trace systems?". The answer was vague: "to have visibility". A sequence of knowledge transfer (Ta + Ti) + (S) led to 3 research works based on qualitative methods.

The first one explored SC maps and SC mapping activities in the company to specify SC visibility needs. Academics asked for Ti and collected data with reflexive interviews with practitioners (Gp). Data analysis (Gr) produced intermediary (P) with restitution (Ta). The results – with "surprises" – were discussed during a focus group with (S) that led to (Gr + p) and (P). The unexpected results of this work led to another "back-stage" pure theoretical reflection by academics (Gr) with (P).

The objective of the second research was to deepened knowledge about the concept of SC visibility. A literature review (Gr) combined with individual reflexive thinking from the experience of people in the company (Gp + Ti) led to analysis (Gr) and (P). (Ta) of the results had important consequence for action (Gp). It reveals SC visibility as the core co-constructed "conceptual space" of the research.

The third work complemented the conceptual space with a synthesis (Gr) of the concept of value with (P). Discussions with (S) led to (Gp + r). The overall analysis for the PhD, linking track and trace, SC visibility and value, is in progress (P1 finished end of 2021 and the PhD is to be defended in 2022).

Lessons from P2.

P2, which scope is the end-to-end SC, questions the relevance of improving both SC robustness and resilience to face risk, uncertainties, and crisis and how to do

*The Dynamic of Knowledge Creation in Joint Industry-Academia Research Projects… DOI: http://dx.doi.org/10.5772/intechopen.101985*

so. The topic was proposed by academics just before the beginning of the covid-19 pandemic. It has been quickly accepted considering the need for both partners to learn from this special crisis. P2 project was mainly based on an academic literature review (Gr) with (P). A kick-off industry-academia meeting launched the project: conceptual basis for the research has been proposed (Ta) and interactive questions and answers resulted in (S). Discussions show the need to stabilize a common conceptual basis to favor dialogue and co-construction of "useful" knowledge.

The pandemic context (covid "waves") put pressure on practitioners and researchers and imposed the agenda and method for the first data collection. Qualitative interviews were the opportunity to (S) about the concepts and to foster (Ti + Gp) to collect experience of covid first wave. Back-stage analysis by academics (Gr) produced intermediary (P). Another industry-academia meeting with intensive co-preparation with (S), mixed (Ta + Ti + S + Gr + p), leading to refined results (P).

Because of the pandemic, it had been difficult up-to-now to develop the interactions in the practice sphere with the PhD student. However, the research and practice spheres could benefit from frequent online meeting with (S) leading to (Ta + Ti) but could not end yet with (Gr + p). However, the PhD student could participate in crisis working groups which is a first step toward more engaged and collaborative action research.

### **4.3 Combined lessons from the two projects**

### *4.3.1 Knowledge creation dynamic: about KM processes and role of action research*

The analysis of P1 and P2 confirms there are different knowledge processes at work that combine and end with knowledge creation (**Figure 1a**). It is valuable to distinguish transfer from sharing and generation from creation (the result). Iterative transfers (Ta + Ti) are very different from sharing (S) in an interactional practice and/or research space.

(Ta) was the first knowledge process at work in the two projects, clearly expected by the company. It was necessary to trigger the research process and stimulated others knowledge processes.

### **Figure 1.**

*Knowledge creation dynamic. a. Knowledge processes interplay. b. Knowledge generation variety. T (knowledge transfer): collaborative research leads to a T dynamic (succession of exchanges Ta, Ti and Ta + Ti). T–>S (knowledge sharing): T calls for conversation, dialog, turning into S. S: co-working in action and/or research leads to a S dynamic. S–>T: S stimulates T (one-to-one or to-many – dissemination). T–>G (knowledge generation): T (specifically Ti collected by academics or Ta) provides basis for G (especially Ti–>Gr; Ta–>Gp). S–>G: S (specially by I + A in the P + R sphere – see Figure 1b) stimulates G. S–>G leads to more "surprises" than T–>G. G: action research leads to a G dynamic combining three G spheres and G actors (Figure 1b) ending with Gp, Gr, Gp + r, the later leading to the greatest "surprises" in terms of C (knowledge creation). G–>T: in action research there is a systematic T of any G (communication, dissemination). G–>S: G sometimes demands discussion, dialog to deepen reflection.*

The overall analysis of knowledge processes sequences in P1 and P2 leading to knowledge creation (with P) unveils the interplay between knowledge processes. **Figure 1** outlines a framework of knowledge creation dynamic.

The results not only deepen KM studies but also AR studies. Our research refines the analysis of research and practice spheres interplaying [30–33].

Compared to previous projects [5], the action research approach proved to boost knowledge creation thanks to industry-academia co-working *in action* (in our cases for the PhD student) and *in research*. Knowledge creation benefits from the combination of *knowledge* and *knowing* [19, 20], and from a more balanced industry-academia relationship [27]: knowledge of academics or practitioners, as well as knowledge generated in the research and/or action sphere are equally valuable, and benefit from being blended. Nevertheless, action research confirms to be time-consuming (academics and managers need time to get used to each other, learning takes time, knowledge creation dynamic is time-costing) with important methodological challenges.

### *4.3.2 Facilitators, barriers to KM processes and their dynamic*

The *in vivo* test of guidelines adopted from [5] and of action research confirms they can be considered as valuable in the context joint industry-academy research projects. Even if our objective was not focused on facilitators and barriers, during our reflective and reflexive analysis we identified factors worth noting.

The role of the SC expert leader and industry supervisors reveals very important, especially their support since the beginning and all along the projects, and the animation with the rest of the company (promoting the project, boosting participation of people in the projects, fostering intra-organizational interactions, and contributing to expand knowledge transfer, sharing and generation in the company and SC partners).

Prior experience of partners in joint industry-academia research projects is another important point as well as their learning orientation and culture, including experiential learning [28], with cognitive (noticing, pay attention), affective (feeling, be astonished) and behavioral (acting, tell about it) capabilities [32]. Their efforts to learn from experience and draw progress upon it had a direct impact: experience during P1 clearly served P2 (especially concerning the care to build the conceptual and relational spaces of the project).

The overall context of the projects plays a key role. It can boost the willingness to create knowledge (example in P2), or constraints interactions, dialogue, and co-construction and knowledge processes (example the covid pandemic for P1 and P2).

Because the PhD student is a cornerstone of such projects with impact on the knowledge processes dynamic, the relationships between the co-supervisors and the frequency of the triadic interactions (PhD student and co-supervisors) are crucial. They impact the research process and the PhD student learning process.

The PhD student's vision of its role in the process is also very important. With regards to the participatory nature of the projects, the question of how he/she sees its knowledge power has a strong influence on (Ti), (S), and (Gr + p).

The conceptual space is a key resource in such projects [29]. Without a common and clear conceptual basis, it is difficult to dialogue and co-produce knowledge. The co-construction of the conceptual space is a key issue that, in P1 and P2, benefited from a rich state-of-the art from academics (Gr + Ta + S).

The projects confirmed the importance of the interactional space and "*Ba"* for dialogue and co-construction. There are key enabling persons, tactics (example industry-academia meetings), or methods (example focus groups) that stimulate, *The Dynamic of Knowledge Creation in Joint Industry-Academia Research Projects… DOI: http://dx.doi.org/10.5772/intechopen.101985*

develop, and maintain their quality. The covid pandemic showed the sensitiveness of this space and the need to maintain it. Remote online meeting using video conferencing systems changed the interactions, but the frequency of industry-academia exchanges increased, and the audience could be developed (example in P2 industryacademic meetings), stimulating Ta + Ti + S (example sharing papers, news, data that would not have been shared in "normal" circumstances).

In both projects, the knowledge processes dynamic is undoubtedly stimulated by intermediary productions all along the project process, whatever their form, audience, and degree of achievement.

In such projects, the knowledge creation needs to alternate on-stage/back-stage [23, 25] work and give time to be reflective and reflexive [21]. The "iterative cycle of action and reflection" [27] by academic and/or practitioners is core to the dynamic.

Such projects demand to be able to mobilize – sometimes in an opportunistic way – a wide range of methods or tactics to adapt to an ever-changing context.

## **5. Conclusion**

This chapter combines our experience in running joint industry-academia research projects in the domain of logistics and SCM, a review of the KM literature focused on knowledge processes, an analysis of action research approaches, and the reflective/reflexive experience from two ongoing action research joint industryacademia research projects with a company. Considering the contexts of the two projects, action research appears like an adequate way of producing knowledge in volatile, uncertain, complex, and ambiguous (VUCA) contexts, to address global challenges.

The research has several theoretical contributions and managerial implications. It provides a rigorous conceptual basis to study four distinct KM processes: knowledge creation, generation, sharing and transfer. The in-depth analysis of the dynamic of knowledge creation confirms the complementary nature of these KM processes and gives insights about the interactions/relationships between them. This confirms the importance of adopting a holistic perspective, not reduced to a unique KM process, and the relevance of articulating/bridging different knowledge views. From a methodological point of view, the micro-KM processes identified and used to code knowledge creation episodes (Ta, Ti, S, Gr, Gp, Gr + p) can be reused in another research. The framework proposed in **Figure 1** is an important grid of reading for academic and practitioners. It reveals the knowledge creation dynamic at a micro-level: the interplay of KM processes as well as of industry and academia actors, the interlocked nature of research and practice spheres. The research also confirms the value of action research as a class of research approaches for joint industry-academia projects but highlight some challenging points. It stresses how important are: the conceptual and interactional spaces, the robustness of research methods, the discussion about intermediary productions, and the efforts of key persons to maintain the interplay of actors, even if it is time-consuming. The research also suggests taking care of the iterative on-stage/back-stage work necessary to articulate action and reflection to create knowledge.

The results presented in this chapter not only complement KM studies, deepening the study of knowledge processes and of their interactions, but also action research studies, combining different approaches and reporting from *in vivo* experiences. It also bridges KM and AR studies showing that action research boost knowledge creation in joint industry-academia research projects.

Beyond the understanding of knowledge processes in joint industry-academia research projects, the results suggest another issue. The KM literature as well as the logistics and SCM literature stress the difference between doing activities because you have to and doing them *consciously to create value*. In line with [8] which "defines firm's KM practices as the conscious organizational and managerial practices intended to achieve organizational goals through efficient and effective management of the firm's knowledge resources", an overall question can be raised: could/ should knowledge processes be consciously managed in joint industry-academia research projects? Could/should these projects explicitly include a *deliberate* KM strategy? Would a conscious approach of KM foster knowledge processes and their dynamic? Since joint industry-academia research projects make part of the partners' knowledge strategy – although more implicitly than explicitly – another question could be raised. Should joint industry-academia research project be *consciously* considered by research partners as making part of their KM strategy?
