The value and significance of corporate community relations: an Italian SME perspective

Purpose – This paper investigates the link between community of place and small and medium-sized enterprises (SMEs) in Lombard industrial districts in Italy. Design/methodology/approach – A brief literature review of international authors from the stakeholder approach and Corporate Community Relations field is presented. This paper refers to a survey of Lombard industrial districts conducted by ALTIS. The data was collected via a telephone survey from 834 firms. Findings – The main finding is that managing Corporate Community Relations (CCR) is of major importance for company success. The results of the survey show that there are some tools and actions that Italian industrial district SMEs uses to interact with their particular communities of place to develop effective and coherent relationships with their stakeholder groups. Moreover, although the survey shows that though SMEs do implement different CCR activities, they are not able to communicate these effectively through systematic communication strategies. However, the narrow sample includes only a sample of some Lombard districts. Nonetheless, the findings indicate that effective CCR seems to confer competitive advantage based on stakeholder responses and rewards sought. Research limitations/implications – The framework could assist in supporting CCR developments between industrial districts as various players would know how to improve CCR activities. One further suggestion is that University and Research Centres could have a role to play in creating and communicating codified knowledge concerning community relations in industrial districts, while other public players still have to develop specific tasks in improving infrastructures. Originality/value – This study is in line with the main focus of CCR, which is in striving to meet stakeholder and societal needs. However, industrial district SMEs have to learn how to communicate their CCR activities from the examples set by large Italian companies. The paper links the notion of CCR with tools and actions to develop meaningful relationships with both community of place and interest. Moreover, considering the survey results, a new framework for local player roles is proposed

The effect of (A) mean sequencing depth (genome coverage) and (B) average number of nucleotide differences per site (<i>π</i>) on accuracy of genetic similarity estimates in simulations.

<p>We plot mean ± standard deviation of Spearman’s <i>ρ</i> comparing each metric to known truth across 20 replicate runs. (A) Mean sequencing depth varies while average number of nucleotide differences per site (<i>π</i>) is constant at 0.005. kWIP: At low to moderate mean sequencing depth (<30x) weighting increases accuracy. The weighted metric (“WIP”) obtains near-optimal accuracy already at 10x and hence much earlier than the unweighted metric “IP”). There is no noticeable decrease in accuracy with increasing coverage. mash: regardless of error correction, mash performs less well than WIP. mash shows accuracy maxima at 4x coverage without (“Mash”) and at 16x coverage with abundance filter (“Mash (AF)”), at which point Mash (AF) performs almost as well as WIP. The accuracy of mash decreases dramatically when coverage is further increased. (B) Genome coverage is kept constant at 8x and average number of nucleotide differences per site (<i>π</i>) varies. While all metrics perform equally at a (<i>π</i>) of 1 in 100 (0.01), the performance of IP, Mash and Mash (AF) decreases rapidly as (<i>π</i>) between samples decreases. This does not occur for the weighted metric (WIP).</p

Overview of the weighted inner product metric as implemented in kWIP.

<p>(A) <i>k</i>-mers are counted into sketches (using khmer [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005727#pcbi.1005727.ref028" target="_blank">28</a>]). Columns represent the “bins” in each sketch. The frequencies of non-zero counts across a set of sketches is computed, forming the population frequency sketch (denoted <i>F</i>). We calculate Shannon entropy of this frequency sketch as the weight vector for the WIP metric (denoted <i>H</i>, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005727#pcbi.1005727.e009" target="_blank">Eq 2</a>). (B) Illustration of Shannon Entropy as used in kWIP: the relationship between the population frequency (<i>F</i>) and the weight (<i>H</i>).</p

Figure 2 - source data 1

TE insertions in all 216 Arabidopsis accession

Estimation of similarity between metagenome samples.

<p>We used kWIP to examine 16S rDNA amplicon sequencing data of Edwards, <i>et al.</i> [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005727#pcbi.1005727.ref035" target="_blank">35</a>] and compare our kWIP result (“kWIP”) with the results as presented by Edwards, <i>et al.</i> (“Weighted UniFrac” and “UniFrac”). We find that kWIP replicates their observations of stratification of root-associated microbiomes by rhizo-compartment (PC1) and experiment site (PC2).</p

Weighting improves the accuracy of replicate clustering.

<p>(A) and (B) show a representative example, demonstrating that (A) the weighted metric (WIP) correctly clusters all sets of 6 replicate runs into their respective samples (indicated by blue and green bars) while (B) the unweighted metric (IP) fails to cluster several replicates correctly (indicated by red highlighting). (C) rank correlation coefficients to expected relationships over 100 sets of 96 rice runs for the WIP and IP metrics. The Weighted metric tends to cluster the replicates better.</p

Computational performance of kWIP.

<p>Computational performance of kWIP.</p

Figure 2 - source data 4

TE family enrichments for TE insertion and TE deletion variant

Figure 1 - source data 2

TE absences in Le