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This paper aims to describe an interdisciplinary and innovative research conducted in Switzerland, at the Geneva School of Business Administration HES-SO and supported by the State Archives of Neuchâtel (Office des archives de l'État de Neuchâtel, OAEN). The problem to be addressed is one of the most classical ones: how to extract and discriminate relevant data in a huge amount of diversified and complex data record formats and contents. The goal of this study is to provide a framework and a proof of concept for a software that helps taking defensible decisions on the retention and disposal of records and data proposed to the OAEN. For this purpose, the authors designed two axes: the archival axis, to propose archival metrics for the appraisal of structured and unstructured data, and the data mining axis to propose algorithmic methods as complementary or/and additional metrics for the appraisal process.

Based on two axes, this exploratory study designs and tests the feasibility of archival metrics that are paired to data mining metrics, to advance, as much as possible, the digital appraisal process in a systematic or even automatic way. Under Axis 1, the authors have initiated three steps: first, the design of a conceptual framework to records data appraisal with a detailed three-dimensional approach (trustworthiness, exploitability, representativeness). In addition, the authors defined the main principles and postulates to guide the operationalization of the conceptual dimensions. Second, the operationalization proposed metrics expressed in terms of variables supported by a quantitative method for their measurement and scoring. Third, the authors shared this conceptual framework proposing the dimensions and operationalized variables (metrics) with experienced professionals to validate them. The expert’s feedback finally gave the authors an idea on: the relevance and the feasibility of these metrics. Those two aspects may demonstrate the acceptability of such method in a real-life archival practice. In parallel, Axis 2 proposes functionalities to cover not only macro analysis for data but also the algorithmic methods to enable the computation of digital archival and data mining metrics. Based on that, three use cases were proposed to imagine plausible and illustrative scenarios for the application of such a solution.

The main results demonstrate the feasibility of measuring the value of data and records with a reproducible method. More specifically, for Axis 1, the authors applied the metrics in a flexible and modular way. The authors defined also the main principles needed to enable computational scoring method. The results obtained through the expert’s consultation on the relevance of 42 metrics indicate an acceptance rate above 80%. In addition, the results show that 60% of all metrics can be automated. Regarding Axis 2, 33 functionalities were developed and proposed under six main types: macro analysis, microanalysis, statistics, retrieval, administration and, finally, the decision modeling and machine learning. The relevance of metrics and functionalities is based on the theoretical validity and computational character of their method. These results are largely satisfactory and promising.

This study offers a valuable aid to improve the validity and performance of archival appraisal processes and decision-making. Transferability and applicability of these archival and data mining metrics could be considered for other types of data. An adaptation of this method and its metrics could be tested on research data, medical data or banking data.

Despite all the attempts developed so far to measure corporate social performance in the last decades, a standard metric for it is still missing. In this work, the author tries to understand why is this the case. To do so, the author has reviewed 69 relevant metrics developed in the literature since the 1970s until today, covering approaches based on social, reputational, and environmental ratings, as well as several others constructed ad hoc by reputated scholars. The author analyzes each of them through a double optics, checking if they meet the minimum requirements to be considered standard and truly social. The research reveals that the main factor that prevents such a standard is the lack of truly social orientation of the existing metrics.

Despite a legacy of research that emphasizes contradictions and their role in explaining change, less is understood about their character or the mechanisms that support them. This gap is especially problematic when making causal claims about the sources of institutional change and our overall conceptions of how institutions matter in social meanings and organizational practices. If we treat contradictions as a persistent societal feature, then a primary analytic task is to distinguish their prevalence from their effects. We address this gap in the context of US electoral discourse and education through an analysis of presidential platforms. We ask how contradictions take hold, persist, and might be observed prior to, or independently of, their strategic use. Through a novel combination of content analysis and computational linguistics, we observe contradictions in qualitative differences in form and quantitative differences in degree. Whereas much work predicts that ideologies produce contradictions between groups, our analysis demonstrates that they actually support convergence in meaning between groups while promoting contradiction within groups.

This chapter integrates research that highlights and demonstrates the importance of the marketing mix and customer attitudinal metrics in influencing the customer’s path to purchase. A key objective of this chapter is the provision of an integrative conceptual framework that links marketing actions to customer mindset metrics along the consumer’s path to purchase and the identification of the mechanisms by which customer mindset metrics contribute to consumer purchase journey. Specifically, it delineates two routes for the effects to manifest on sales: the “mindset route” where marketing actions influence customer mindset metrics, which in turn influence brand performance, or the “transactions route” where marketing actions influence market performance directly without influencing the intermediate mindset metrics. A second objective is to identify empirical patterns on incorporating marketing mix and mindset metrics along the path to purchase by reviewing key papers in this domain. Finally, the chapter concludes with the formulation of a rich, forward-looking research agenda on the customer mindset metrics – path to purchase link.

While metrics are becoming increasingly important for marketing’s relevance, there is also a need to understand how they, as enablers of learning, affect marketing’s adaptive capabilities that ensure its long-term success. Therefore, this study aims to test the association of marketing and financial metrics use and the metric-based orientations of training and compensation, with two key marketing routines – exploitation, i.e. the perfecting of existing activities while allowing for incremental adaptations and exploration or experimentation accompanied by radical adaptation.

The study gathers data from 205 managers and uses partial least squares structural equation modeling to test the hypothesized relationships.

Marketing metrics encourage both forms of marketing adaptation. Financial metrics use discourages exploration. Market orientation and long-term orientation strengthen (weaken) the positive (negative) relationship between marketing (financial) metrics use and marketing exploration. Metric-based training is more positively associated with both adaptive capabilities than a metric-based compensation orientation, albeit weakly.

The study’s central proposition – that different metrics or metric orientations are associated with distinct types of knowledge, interpretations, mindsets, motivations and cultural contexts – provides a deeper theoretical understanding of the pathways by which a metric emphasis affects marketing adaptation.

Marketing managers should emphasize marketing metrics and training more than compensation, to promote marketing exploitation/exploration, while exercising caution in overstressing financial metrics given their negative association with exploration. This latter negative relationship can be weakened (as can the positive one between marketing metrics and exploration be strengthened) with increased market orientation and long-term orientation.

This study addresses the research gap regarding the relationship between metrics as a configurational element of marketing organization and marketing adaptation.