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Abstract
This article contributes to the literature by carrying out the first empirical investigation into the role of different types of enterprises in the creation of social trust. Drawing on a unique data set collected through the administration of a questionnaire to a representative sample of the population of the Italian Province of Trento in March 2011, we find that cooperatives are the only type of enterprise where the work environment fosters the social trust of workers.
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Notes
Recent data on Italian cooperatives confirm these statements. On the basis of information drawn from the AIDA database (Bureau Van Dijk 2012), 23,146 cooperatives were created in Italy between 2007 and 2011. Among these, 18,822 were active in 2010, 15,097 passed a balance sheet, and 12,555 had net results in the positive in the same year. Given the fact that the total number of active Italian cooperatives rests between 80,000 and 84,000 units, the number of coops has increased by about 15 % during the period of the crisis, employing about 150,000 workers, and made 5 billion euros in revenue in 2010. The total revenues of all Italian cooperatives increased from about 83 billion euros in 2007 to about 97 billion euros in 2011 (an increase of about 17 %). In the same period, however, net revenues decreased from about 850 million euros to 100 million, signaling the severe difficulties that the crisis is imposing on these organizations. On the other hand, between 2007 and 2011 employment in cooperatives increased by about 8 %, reaching 1.34 million, 7.2 % of the total Italian workforce. Cooperatives have been growing, preserving and creating new employment during the crisis, even at the cost of dangerously squeezing margins. This way they are fulfilling a countercyclical role.
Cooperative enterprises can also be small organizations, as happens in producer and agricultural cooperatives. However, this evidence does not fundamentally modify the personal character of membership of cooperatives since, on the one hand, small producers and farmers very often coincide with individual or family firms (Valentinov2007). On the other hand, in legal terms, membership rights are given to organizations as legal persons and not to the capital invested in or by these legal persons.
It is worth noting that the spread of trust inside the organization can help to solve the new-institutionalist dilemma concerning the growth of transaction costs in terms of ownership and governance costs. In principle, horizontal, non-hierarchical decision-making processes can be expected to be less coordinated and more time and resource expensive than hierarchical ones. However, this inefficient outcome may not be observed in reality. Trust relations, tacit knowledge and informal interpersonal relations can work as substitutes of hierarchy in supporting expeditious and effective organizational outcomes; democratic governance can reduce, not inflate, transaction costs. Furthermore, for similar reasons, inclusive organizational relations are also expected to reduce agency costs, that is, the costs associated with asymmetric information and contrasting objectives (Alchian and Demsetz1972; Jensen and Meckling1976).
We do this by concentrating on two main variables: the condition of being a worker employed in a cooperative firm and the self-evaluated generation of trust toward other people due to working conditions. In doing so, we correlate one objective dummy with one subjective rating. The correlation between objective and subjective variables is usually considered immune to spurious correlation due to common method bias, as evidenced by prominent methodological contributions (Podsakoff et al.2003).
The questionnaire was administered through computer-assisted telephone interviews by the Technical Unit of the Department of Sociology and Social Research of the University of Trento. The administration of the questionnaire was funded by the European Research Institute on Cooperative and Social Enterprises (Euricse), located in Trento. Since, according to the research design, about 800–900 observations were required, a sample of 8,855 units (i.e., about ten times the number of required observations) was extracted from census data. People included in the selected sample received a letter in advance announcing the possibility of a phone interview and briefly describing the aim and scope of the research. There were 1,587 dropouts, 1,777 people refused to be interviewed, 136 people missed the phone appointment, and 162 phone numbers were not in use at the time of the interview; 4,396 numbers were not used.
Results do not change if we consider the smaller sample of current workers.
The sample includes 40 workers who were employed in cooperative enterprises at the moment of the interview. Thirteen (32.5 %) of them were employed in credit cooperatives, 7 in worker cooperatives (17.5 %), 7 in social coops, 6 in agricultural coops (15 %), 3 in consumer coops and 4 in “other types” of coops (e.g., services or housing cooperatives).
Frequency distributions for public and private enterprises are not reported here for the sake of brevity. Tables are available upon request to the authors.
This question was asked of all workers with job experience, i.e. current workers, retired workers and currently unemployed workers.
We use theCategorical Principal Component Analysis (CatPCA; Meulman et al.2004) for quantifying ordinal categories, with the number of the componentsp = 2, the number of the assumed subdimensions for the job motivations. The optimal quantifications are assigned to the categories of each item minimizing (by means of an alternating least squares algorithm) the following loss function simultaneously overO and theYjs:
\( L(O,Y) = \sum\nolimits_{j = 1}^{m} {{\text{tr}}||O - G_{j} Y_{j} ||^{2} } \)
with tr||·||2 the trace operator of the squared norm of a matrix,Gj the indicator matrix of itemj,O then × p matrix of object scores for then subjects, andYj the matrix containing the category quantifications of itemj. As goodness of fit statistics, we consider the generalized Cronbach’s alpha (GCA) index and the variance accounted for (VAF) index, which are normalized [in the interval (0;100)] indices based on the total eigenvalue of the CatPCA solution. The quantified variables obtained from the CatPCA are then used for the standardexploratory factor analysis (EFA) to identify the hypothesized sub-dimensions by inspecting the factor loadings of the rotated solution.
In the EFA, the extraction method is principal axis factoring. This allows us to concentrate on the variance shared by the latent dimensions, not on total variance. This clarifies the relatively low percentage of total variance explained by the two factors with eigenvalues higher than one (about 42 %). We also performed factor analysis by using principal components as the extraction method. The results do not change qualitatively but the amount of variance explained by the first two factors is 55 %. We extract the rotated solution using the Varimax method with Kaiser normalization, which is preferred to the Oblimin method because it allows the analysis of the two latent dimensions as independent (orthogonal) dimensions. This assumption eases the analysis though in practice we cannot exclude a non-zero correlation between intrinsic and extrinsic motivations.
Possible responses to these questions were given on a scale from 1 = “I do not have relatives/friends” to 7 = “every day,” with 2 = “never,” 3 = “a few times per year,” 4 = “a few times per month,” 5 = “once per week” and 6 = “more than once per week.” As for meetings with relatives, interviewers were explicitly required to refer to non-cohabiting relatives.
We accounted for local politicians instead of politicians in general, because the Province of Trento has autonomous jurisdiction relative to the Italian state on most social issues. Hence, we consider the provincial rather than the national context as the relevant unit of political analysis.
As a further check, we performed all the regressions presented in Sect.4, including IMRs among regressors. Their coefficients were always not statistically significant. Results of regressions are not presented in the article for the sake of brevity and are available upon request to the authors.
In our study, it appears that democratic governance (characterizing cooperative firms) more than a socially beneficial objective (characterizing nonprofit organizations) is the main factor supporting the development of trust inside the organization. The interactional context defined by the presence of membership rights appears particularly beneficial in this context. On the other hand, the evidence available to us does not allow us to draw clear conclusions about the influence exerted by the social aim and by the not-for-profit nature of nonprofit organizations.
Results do not show any significant change if we perform the regressions in the subsample of current workers. The marginal effect of employment in cooperative enterprises on the work-driven development of trust is 0.24, thet value being 2.95. Workers in cooperative enterprises exhibit a 24 % point higher likelihood that work has driven an increase in their social trust from 51 %. The marginal effect of intrinsic motivations is equal to 0.16 (t value is equal to 5.36). Full estimates are not presented here for the sake of brevity and are available upon request to the authors.
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Acknowledgments
We are deeply indebted to Carlo Borzaga, whose advice helped to improve the design of the research in a number of ways. Beatrice Valline and Sara Depedri provided excellent assistance in the development of the questionnaire. We are grateful to two anonymous referees whose comments allowed a decisive improvement of the article. Alessandra Gualtieri, Giulia Galera and Matteo Rizzolli also provided useful suggestions.
Author information
Authors and Affiliations
Faculty of Economics, Department of Economics and Law, Sapienza University of Rome, via del Castro Laurenziano 9, 00161, Rome, Italy
Fabio Sabatini
European Research Institute on Cooperative and Social Enterprises (Euricse), Trento, Italy
Fabio Sabatini, Francesca Modena & Ermanno Tortia
Laboratory for Comparative Social Research, National Research University Higher School of Economics, Moscow, Russia
Fabio Sabatini
Department of Economics and Management, University of Trento, Trento, Italy
Francesca Modena & Ermanno Tortia
- Fabio Sabatini
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- Francesca Modena
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Correspondence toFabio Sabatini.
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The empirical analysis in this paper is based on data collected within a research project promoted and funded by the European Research Institute on Cooperative and Social Enterprises (Euricse), Trento. The findings, interpretations and conclusions expressed in the paper are solely of the authors and do not necessarily represent the views of Euricse.
Appendices
Appendix 1: Categorical principal component analysis and factor analysis
Categorical principal component analysis
Dimension | Cronbach’s alpha | Variance accounted for total (eigenvalue) |
|---|---|---|
Model summary | ||
1 | .789 | 3.350 |
2 | .357 | 1.464 |
Total | .891a | 4.814 |
- Total Cronbach’s alpha is based on the total eigenvalue
V3410_1 | V3410_2 | V3410_3 | V3410_4 | V3410_5 | V3410_6 | V3410_7 | V3410_8 | V3410_9 | |
|---|---|---|---|---|---|---|---|---|---|
Correlation transformed variables | |||||||||
V3410_1a | 1.000 | .320 | .340 | .252 | .198 | .090 | .247 | .045 | .049 |
V3410_2a | .320 | 1.000 | .258 | .209 | .216 | .080 | .209 | .047 | .158 |
V3410_3a | .340 | .258 | 1.000 | .280 | .259 | .171 | .248 | .254 | .153 |
V3410_4a | .252 | .209 | .280 | 1.000 | .322 | .284 | .314 | .288 | .222 |
V3410_5a | .198 | .216 | .259 | .322 | 1.000 | .440 | .395 | .445 | .386 |
V3410_6a | .090 | .080 | .171 | .284 | .440 | 1.000 | .426 | .444 | .512 |
V3410_7a | .247 | .209 | .248 | .314 | .395 | .426 | 1.000 | .381 | .405 |
V3410_8a | .045 | .047 | .254 | .288 | .445 | .444 | .381 | 1.000 | .458 |
V3410_9a | .049 | .158 | .153 | .222 | .386 | .512 | .405 | .458 | 1.000 |
Dimension | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
Eigenvalue | 3.264 | 1.432 | .804 | .715 | .680 | .601 | .553 | .509 | .441 |
- Missing values were imputed with the mode of the quantified variable
Dimension | ||
|---|---|---|
1 | 2 | |
Component loadings | ||
V3410_1 | .389 | .688 |
V3410_2 | .382 | .564 |
V3410_3 | .526 | .456 |
V3410_4 | .592 | .209 |
V3410_5 | .725 | −.093 |
V3410_6 | .707 | −.376 |
V3410_7 | .705 | −.028 |
V3410_8 | .675 | −.356 |
V3410_9 | .669 | −.379 |
- Variable principal normalization
Factor Analysis
Initial | Extraction | |
|---|---|---|
Communalities | ||
V3410_1 quantification | .243 | .446 |
V3410_2 quantification | .203 | .271 |
V3410_3 quantification | .262 | .355 |
V3410_4 quantification | .231 | .284 |
V3410_5 quantification | .402 | .470 |
V3410_6 quantification | .434 | .552 |
V3410_7 quantification | .337 | .400 |
V3410_8 quantification | .409 | .498 |
V3410_9 quantification | .415 | .496 |
- Extraction method: principal axis factoring
Factor | Initial eigenvalues | Extraction sums of squared loadings | Rotation sums of squared loadings | ||||||
|---|---|---|---|---|---|---|---|---|---|
Total | % of variance | Cumulative % | Total | % of variance | Cumulative % | Total | % of variance | Cumulative % | |
Total variance explained | |||||||||
1 | 3.453 | 38.371 | 38.371 | 2.897 | 32.184 | 32.184 | 2.368 | 26.310 | 26.310 |
2 | 1.461 | 16.230 | 54.601 | .874 | 9.715 | 41.899 | 1.403 | 15.589 | 41.899 |
3 | .762 | 8.461 | 63.063 | ||||||
4 | .703 | 7.816 | 70.879 | ||||||
5 | .661 | 7.340 | 78.219 | ||||||
6 | .568 | 6.315 | 84.534 | ||||||
7 | .538 | 5.982 | 90.515 | ||||||
8 | .472 | 5.250 | 95.765 | ||||||
9 | .381 | 4.235 | 100.000 | ||||||
- Extraction method: principal axis factoring
Factor | ||
|---|---|---|
1 | 2 | |
Rotated factor matrixa | ||
V3410_1 Quantification | .668 | |
V3410_2 Quantification | .513 | |
V3410_3 Quantification | .551 | |
V3410_4 Quantification | .360 | .393 |
V3410_5 Quantification | .607 | .318 |
V3410_6 Quantification | .738 | |
V3410_7 Quantification | .537 | .334 |
V3410_8 Quantification | .698 | |
V3410_9 Quantification | .700 | |
- Extraction method: principal axis factoring
- Rotation method: Varimax with Kaiser normalization
- aRotation converged in three iterations
Appendix 2
See Table 6.
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Sabatini, F., Modena, F. & Tortia, E. Do cooperative enterprises create social trust?.Small Bus Econ42, 621–641 (2014). https://doi.org/10.1007/s11187-013-9494-8
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Keywords
- Cooperative enterprises
- Nonprofit organizations
- Trust
- Social capital
- Motivations
- Inclusive governance
- Work organization