How to Effectively Institutionalize Social Selling in Business-to-Business Companies

A new sales approach called social selling is gaining momentum in Business-to-Business (B2B) practice. Instead of one-to-one interactions (e.g., physical visits), nowadays, salespeople reach out to their (potential) B2B customers in one-to-many social media interactions. Salespeople regularly employ their private social media accounts (e.g., LinkedIn) to gather information about and to interact with their (potential) buyers. So far, research offers only a limited, one-sided and static view of this new sales phenomenon. Relying on a qualitative study with 40 managers from sales, social media, and the C-suite, the current paper adopts a cross-functional and procedural view to more holistically investigate the concept of social selling and its institutionalization in B2B companies. Our data distills a five-stage process and provides insights on core topics, activities, company prerequisites, and potential pitfalls for each stage of the institutionalization process. These findings may help managers to more effectively institutionalize social selling in B2B companies

Total Allowable Catch (TAC) and quota management system in the European Union

SIGLEAvailable from British Library Document Supply Centre- DSC:3597.6628(AU-DE-DP--94-18) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Toward a Differentiated Understanding of the Value‐Creation Chain

The conventional view of the value-creation chain suggests offering high-value propositions at the product level (in terms of benefits provided by elements of the product) to attain high-value perceptions at the customer level, which should ultimately result in high-value appropriation at the firm level (i.e. relationship, volume, pricing and financial success). This study challenges this view and provides a differentiated understanding of the value creation chain. With a multi-industry sample of 339 companies and a sample of 626 customers to validate managerial assessments, the authors apply a configurational approach to identify whether and to what extent offering high-value propositions at the product level is necessary or sufficient for achieving superior value perceptions at the customer level and high-value appropriation at the firm level. Taking into account the company-internal and company-external environment of the value-creation chain, the study identifies seven value creation chain constellations

Skin Doctor CP : Conformal Prediction of the Skin Sensitization Potential of Small Organic Molecules

Skin sensitization potential or potency is an important end point in the safety assessment of new chemicals and new chemical mixtures. Formerly, animal experiments such as the local lymph node assay (LLNA) were the main form of assessment. Today, however, the focus lies on the development of non-animal testing approaches (i.e., in vitro and in chemico assays) and computational models. In this work, we investigate, based on publicly available LLNA data, the ability of aggregated, Mondrian conformal prediction classifiers to differentiate between non- sensitizing and sensitizing compounds as well as between two levels of skin sensitization potential (weak to moderate sensitizers, and strong to extreme sensitizers). The advantage of the conformal prediction framework over other modeling approaches is that it assigns compounds to activity classes only if a defined minimum level of confidence is reached for the individual predictions. This eliminates the need for applicability domain criteria that often are arbitrary in their nature and less flexible. Our new binary classifier, named Skin Doctor CP, differentiates nonsensitizers from sensitizers with a higher reliability-to-efficiency ratio than the corresponding nonconformal prediction workflow that we presented earlier. When tested on a set of 257 compounds at the significance levels of 0.10 and 0.30, the model reached an efficiency of 0.49 and 0.92, and an accuracy of 0.83 and 0.75, respectively. In addition, we developed a ternary classification workflow to differentiate nonsensitizers, weak to moderate sensitizers, and strong to extreme sensitizers. Although this model achieved satisfactory overall performance (accuracies of 0.90 and 0.73, and efficiencies of 0.42 and 0.90, at significance levels 0.10 and 0.30, respectively), it did not obtain satisfying class-wise results (at a significance level of 0.30, the validities obtained for nonsensitizers, weak to moderate sensitizers, and strong to extreme sensitizers were 0.70, 0.58, and 0.63, respectively). We argue that the model is, in consequence, unable to reliably identify strong to extreme sensitizers and suggest that other ternary models derived from the currently accessible LLNA data might suffer from the same problem. Skin Doctor CP is available via a public web service at https://nerdd.zbh.uni-hamburg.de/skinDoctorII/