Exploring Responsible Research and Innovation in reputable agri-food cooperatives and the link to international orientation. An exploratory empirical case study in Spain

Figures & data

Table 1. Technical data sheet.

Universe / Geographical area	Agri-food Cooperatives/Spain
Population	Top 100 cooperatives according to CEPES’ ranking (2018)
Period of gathering information	May-July 2020
Method	Online questionnaire (Google Form)
Sampling unit	Cooperative Managers
Final sample	60 agri-food cooperatives

Source: Own elaboration.

Table 2. RRI dimensions and implication for agri-food cooperatives.

RRI Dimensions	Implications for businesses	Implications for agri-food cooperatives
ANTICIPATION	Awareness of future legislation Better risk management First mover advantage	Due to the regulatory complexity of the agri-food industry, anticipation is essential to access new markets. Social certifications are a common requirement that limits companies’ access to European markets. On the other hand, for many cooperatives, the barrier of internationalization can be overcome with RRI, leading to a reduction of the risk of these activities.
INCLUSION	Transparency Sharing knowledge culture Advantages from collaboration Better ability to match social expectations	The management of internal and external social capital in cooperatives is fundamental for sharing knowledge. Likewise, internationalization requires alliances and collaborations to overcome barriers mainly associated with financial constraints. Also, different actors in cooperatives can be introduced into the RRI process, which implies matching social expectations.
REFLEXIVITY	Critical overview and improvement of innovation performance Higher probability of innovation success	Members’ participation in the cooperative facilitates their involvement in RRI activities. The idea of co-creation partnerships is fully viable in these companies due to their social capital.
RESPONSIVENESS	Increased trust from customers Reputation and public trust in offered goods	In the agri-food industry, the need to ensure consumer confidence is essential. Products that incorporate RRI to improve their quality are useful in this regard.

Source: Own elaboration based on Nazarko (2020).

Table 3. RRI Indicators proposed for agri-food cooperatives.

	Foundations		Our Proposal
	Selection of techniques and approaches from Stilgoe, Owen, and Macnaghten (2013)	Selection of indicators and related activities in smart farming from Eastwood et al. (2019)	RRI Indicators^(1) for agri-food cooperatives ^(1) Likert scale from 0 to 10 points (Strongly Disagree-Strongly agree)
ANTICIPATION	Foresight – Technology assessment	Technology-use surveys, assessing farmer perceptions of technology	We conduct surveys to find out about the technology-use of our members (RRIA1)
	Horizon scanning – Vision assessment	Scenario building – Visioning of smart dairy farms, assessing potential social, animal, and environmental outcomes	We conduct studies about the economic, social and/or environmental potential of our members (RRIA2)
INCLUSION	Consensus conferences – Ability to interrogate the social and ethical stakes associated with new science and technology	Involvement of relevant actors –Socio-ethical discussions about farmer-technology interactions – Critical feedback in workshops	We involve our members in workshops, debates or training courses about the use and interaction with technology (RRII1)
	Quality of dialogue as a learning exercise	Private sector engagement – Private companies included as partners in R&D projects	We engage with private companies to develop R&D projects (RRII2)
	User-centred design – Open innovation	Encouraging transformative mutual learning – Open innovation and co-innovation	We encourage transformative mutual learning through co-innovation, open innovation, or user-centred designs (RRII3)
REFLEXIVITY	Ethical technology assessment	Processes to guide reflection within research teams on underlying assumptions and values around development and use of technology – Reflexive monitors used in project – People dedicated to facilitating reflection	We have research groups and/or specific job positions to promote technology-use (RRIREFI)
	Codes of conduct	Reflexivity is embedded in R&D projects using processes such as codes of conduct and standards – Engagement with codes of conduct	Our R&D is engaged with codes of conduct or best practices guidelines (RRIREF2)
RESPONSIVENESS	Constitution of grand challenges	Potential to adapt projects – Ability to change direction based on stakeholder feedback	We have the ability to change direction based on our direct and permanent link with our members and other stakeholders’ feedback (RRIRSP1)
	Open access and other mechanisms of transparency	Open research processes and access to research data – R&D is transparent, and the process are accessible to private companies, farmers, and communities – Open data exchange, open access to research results, declaring conflicts of interest	We are involved in open and completely transparent R&D projects with data exchange (RRIRSP2)

Source: Own elaboration based on Stilgoe, Owen, and Macnaghten (2013) and Eastwood et al. (2019).

Table 4. Measurement of IO.

IO Indicators	Scale
We intensively seek foreign markets (INT1) We utilize foreign information (INT2) We utilize advanced management skills from foreign countries (INT3) We are focused on developing lasting alliances with foreign partners and/or clients (INT4) We manage our international marketing operations efficiently (INT5)	Likert scale from 0 to 10 points (Strongly Disagree-Strongly agree)

Source: Own elaboration, based on Welch and Luostarinen (1993) and Zhou, Wu, and Luo (2007).

Table 5. Descriptive Statistics for RRI.

Manifest variable	Mean	Median	Min	Max	Standard Deviation	Excess Kurtosis	Skewness
RRIA1	6.033	7	0	10	2.938	−0.875	−0.527
RRIA2	6.85	7	0	10	2.719	−0.029	−0.904
RRII1	6.95	8	0	10	2.929	0.128	−0.966
RRII2	7.25	8	0	10	2.668	0.854	−1.177
RRII3	5.88	7	0	10	2.984	−0.547	−0.614
RRIREF1	7.00	7	0	10	2.961	−0.207	−0.948
RRIREF2	6.40	8	0	10	3.236	−0.670	−0.779
RRIRSP1	7.217	8	0	10	2.450	0.423	−0.983
RRIRSP2	6.717	8	0	10	2.995	−0.128	−0.906

Source: Own elaboration.

Table 6. Correlations between RRI manifest variables.

	RRIA1	RRIA2	RRII1	RRII2	RRII3	RRIREF1	RRIREF2	RRIRSP1	RRIRSP2
RRIA1	1
RRIA2	0.756	1
RRII1	0.724	0.654	1
RRII2	0.530	0.607	0.756	1
RRII3	0.647	0.600	0.754	0.680	1
RRIREF1	0.536	0.708	0.632	0.635	0.632	1
RRIREF2	0.567	0.613	0.686	0.741	0.626	0.682	1
RRIRSP1	0.606	0.698	0.649	0.662	0.583	0.728	0.683	1
RRIRSP2	0.524	0.570	0.720	0.747	0.752	0.690	0.650	0.567	1

Source: Own elaboration.

Table 7. Factor analysis.

Items	Factor 1
RRII1	0.879
RRII2	0.850
RRII3	0.837
RRIREF2	0.834
RRIREF1	0.833
RRIRSP2	0.831
RRIA2	0.826
RRIRSP1	0.823
RRIA1	0.782
% of standard deviation	69.423

Notes:

Kaiser-Meyer-Olkin Index = 0.908

Barlett’s Test of Sphericity (Approx. Chi-square) = 431.305

df = 36

Significance level = 0.000

Source: Own elaboration.

Table 8. CTA-PLs results for RRI data.

Model-implied non-redundant vanishing tetrad	Bootstrap t-value	P Values	Bias	CI Low	CI Up
RRIA1,RRIA2,RRII1,RRII2	1.006	0.315	−0.581	−16.706	34.352
RRIA1,RRIA2,RRII2,RRII1	1.947	0.052	−0.793	−7.626	37.291
RRIA1,RRIA2,RRII1,RRII3	1.458	0.145	−0.587	−10.189	30.322
RRIA1,RRII1,RRII3,RRIA2	0.255	0.798	−0.017	−8.937	10.569
RRIA1,RRII1,RRIREF1,RRIA2	1.888	0.059	−0.487	−6.807	30.241
RRIA1,RRIA2,RRII1,RRIREF2	0.717	0.474	−0.349	−18.526	30.508
RRIA1,RRIA2,RRII1,RRIRSP1	0.156	0.876	−0.132	−17.594	16.170
RRIA1,RRIA2,RRIRSP2,RRII1	1.942	0.052	−0.776	−7.768	37.611
RRIA1,RRIA2,RRII3,RRII2	1.042	0.297	−0.481	−14.843	31.221
RRIA1,RRIA2,RRIREF2,RRII2	1.911	0.056	−0.733	−8.663	39.971
RRIA1,RRIA2,RRIRSP1,RRII2	1.409	0.159	−0.441	−7.961	22.726
RRIA1,RRII2,RRIRSP2,RRIA2	0.435	0.664	0.014	−8.200	6.167
RRIA1,RRIA2,RRIRSP1,RRII3	0.973	0.331	−0.325	−9.584	19.240
RRIA1,RRIA2,RRIREF2,RRIREF1	1.042	0.297	−0.338	−17.049	35.219
RRIA1,RRIA2,RRIREF1,RRIRSP1	1.947	0.052	−0.554	−5.565	27.085
RRIA1,RRIREF1,RRIRSP1,RRIA2	0.874	0.382	0.169	−14.556	7.912
RRIA1,RRIREF2,RRIRSP1,RRIA2	0.398	0.691	−0.072	−10.380	13.586
RRIA1,RRII2,RRII3,RRIREF1	0.804	0.421	0.185	−25.672	14.842
RRIA1,RRII2,RRIREF2,RRII3	1.057	0.290	0.313	−16.141	7.493
RRIA1,RRII2,RRIREF2,RRIRSP2	1.296	0.195	0.391	−20.735	7.995
RRIA1,RRII2,RRIRSP2,RRIRSP1	1.004	0.316	0.122	−10.997	5.452
RRIA1,RRII3,RRIRSP1,RRIREF2	0.657	0.511	−0.190	−16.023	25.075
RRIA1,RRII3,RRIREF2,RRIRSP2	0.054	0.957	0.225	−28.145	26.742
RRIA1,RRIREF1,RRIRSP2,RRIREF2	0.142	0.887	−0.042	−16.703	15.326
RRIA1,RRIRSP1,RRIRSP2,RRIREF1	0.388	0.698	−0.160	−16.077	21.021
RRIA2,RRII2,RRIREF1,RRII1	1.008	0.313	0.510	−39.572	19.447
RRIA2,RRIREF1,RRIRSP1,RRII2	0.495	0.621	−0.136	−7.029	10.007

Source: Own elaboration.

Table 9. Measurement model.

Items	Loadings	Rho-A	Composite Reliability (CR)	Average Variance Extracted (AVE)	HTMT
RRA1	0.773	0.948	0.944	0.653	0.794
RRA2	0.727
RRI1	0.894
RRI2	0.932
RRI3	0.701
RRREF1	0.699
RRREF2	0.814
RRRSP1	0.888
RRRSP2	0.808

Source: Own elaboration.

Table 10. Results for PLSc linking RRI to the IO of cooperatives.

Hypothesis/Relationship	Std. Beta	Std. Dev.	t-value	p-value	2.50%	97.50%	R^2
H1: RRI →INT	0.812	0.069	11.724	0.000	0.67	0.94	0.65

Source: Own.

Nazarko, L. 2020. “Responsible Research and Innovation in Enterprises: Benefits, Barriers and the Problem of Assessment.” Journal of Open Innovation: Technology, Market, and Complexity 6 (1): 12. https://doi.org/10.3390/joitmc6010012.

 Google Scholar

Stilgoe, J., R. Owen, and P. Macnaghten. 2013. “Developing a Framework for Responsible Innovation.” Research Policy 42 (9): 1568–1580. https://doi.org/10.1016/j.respol.2013.05.008

 Web of Science ®Google Scholar

Eastwood, C., L. Klerkx, M. Ayre, and B. D. Rue. 2019. “Managing Socio-Ethical Challenges in the Development of Smart Farming: From a Fragmented to a Comprehensive Approach for Responsible Research and Innovation.” Journal of Agricultural and Environmental Ethics 32 (5-6): 741–768. https://doi.org/10.1007/s10806-017-9704-5

 Web of Science ®Google Scholar

Welch, L. S., and R. K. Luostarinen. 1993. “Inward-outward Connections in Internationalization.” Journal of International Marketing 1 (1): 44–56. https://doi.org/10.1177/1069031X9300100104

 Google Scholar

Zhou, L., W. Wu, and X. Luo. 2007. “Internationalization and the Performance of Born-Global SMEs: The Mediating Role of Social Networks.” Journal of International Business Studies 38 (4): 673–690. https://doi.org/10.1057/palgrave.jibs.8400282

 Web of Science ®Google Scholar