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In this article, the author discusses works from the French Documentation Movement in the 1940s and 1950s with regard to how it formulates bibliographic classification systems as documents. Significant writings by Suzanne Briet, Éric de Grolier and Robert Pagès are analyzed in the light of current document-theoretical concepts and discussions.

Conceptual analysis.

The French Documentation Movement provided a rich intellectual environment in the late 1940s and early 1950s, resulting in original works on documents and the ways these may be represented bibliographically. These works display a variety of approaches from object-oriented description to notational concept-synthesis, and definitions of classification systems as isomorph documents at the center of politically informed critique of modern society.

The article brings together historical and conceptual elements in the analysis which have not previously been combined in Library and Information Science literature. In the analysis, the article discusses significant contributions to classification and document theory that hitherto have eluded attention from the wider international Library and Information Science research community. Through this, the article contributes to the currently ongoing conceptual discussion on documents and documentality.

The purpose of this paper is to increase the understanding regarding how managers attempt to make purposeful use of innovation management self-assessments (IMSA) and performance information (PI).

An interpretative perspective on purposeful use is used as an analytical framework, and the paper is based on empirical material from two research projects exploring the use of IMSA and PI in three case companies. Based on the empirical data, consisting of interviews and observations of workshops and project meetings, qualitative content analysis has been conducted.

The findings of this paper indicate that how managers achieve a purposeful use of PI is related to their approach toward how to use the specific PI at hand, and two basic approaches are analytically separated: a rule-based approach and a reflective approach. Consequently, whether or not the right thing is being measured also becomes a question of how the PI is actually being interpreted and used. Thus, the extensive focus on what to measure and how to measure it becomes edgeless unless equal attention is given to how managers are able to use the PI to make knowledgeable decisions regarding what actions to take to achieve the desired changes.

Given the results, it comes with a managerial responsibility to make sure that all managers who are supposed to be engaged in using the PI are given roles in the self-assessments that are aligned with the level of knowledge they possess, or can access.

How managers purposefully use PI is a key to understand the potential impact of self-assessments.

The paper provides a detailed historical account of Douglass C. North's early intellectual contributions and analytical developments in pursuing a Grand Theory for why some countries are rich and others poor.

The author approaches the discussion using a theoretical and historical reconstruction based on published and unpublished materials.

The systematic, continuous and profound attempt to answer the Smithian social coordination problem shaped North's journey from being a young serious Marxist to becoming one of the founders of New Institutional Economics. In the process, he was converted in the early 1950s into a rigid neoclassical economist, being one of the leaders in promoting New Economic History. The success of the cliometric revolution exposed the frailties of the movement itself, namely, the limitations of neoclassical economic theory to explain economic growth and social change. Incorporating transaction costs, the institutional framework in which property rights and contracts are measured, defined and enforced assumes a prominent role in explaining economic performance.

In the early 1970s, North adopted a naive theory of institutions and property rights still grounded in neoclassical assumptions. Institutional and organizational analysis is modeled as a social maximizing efficient equilibrium outcome. However, the increasing tension between the neoclassical theoretical apparatus and its failure to account for contrasting political and institutional structures, diverging economic paths and social change propelled the modification of its assumptions and progressive conceptual innovation. In the later 1970s and early 1980s, North abandoned the efficiency view and gradually became more critical of the objective rationality postulate. In this intellectual movement, North's avant-garde research program contributed significantly to the creation of New Institutional Economics.

This study aims to analyze communication from the perspective of social marketing, positive emotions, and the topics chosen by Spanish tourist destinations to show their destination image. Additionally, this research shows a message classification model, based on the aforementioned characteristics, that has generated a greater impact, offering clarity to tourism managers on the type of content they should publish to achieve greater visibility.

The methodology used in this work combines content analysis and data mining techniques. The classification tree using the chi-square automatic interaction detector (CHAID) algorithm was selected to determine predictors of like behaviour.

The results show that the predictor variables have been emotions, social marketing and topics. Also, the characteristics of the messages most likely to have a high impact are those related to emotions of joy or happiness, their purpose is behavioural, and they talk about rural, cultural issues, special dates, getaways, or highlights of a town or city for something specific.

This study is the first to analyze the content of the tweets shared by destination tourism managers from a social marketing, positive emotions, and sustainability perspective, determining the possible predictors of likes on Twitter. The authors contribute to the literature by deepening the understanding of how social marketing and the positive emotions promoted drive a more significant impact in tourism communication campaigns on social media. The authors provide destination managers with a way better to understand the variables relevant to users in tourism content.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

The aim of this study is to explore and discuss key challenges associated with having stakeholders take part in co-designing a health care intervention to increase mobility in older medical patients admitted to two medical departments at two hospitals in Denmark.

The study used a qualitative design to investigate the challenges of co-designing an intervention in five workshops involving health professionals, patients and relatives. “Challenges” are understood as “situations of being faced with something that needs great mental or physical effort in order to be done successfully and therefore tests a person's ability” (Cambridge Dictionary). Thematic content analysis was conducted with a background in the analytical question: “What key challenges arise in the material in relation to the co-design process?”.

Two key challenges were identified: engagement and facilitation. These consisted of five sub-themes: recruiting patients and relatives, involving physicians, adjusting to a new researcher role, utilizing contextual knowledge and handling ethical dilemmas.

The population of patients and relatives participating in the workshops was small, which likely affected the co-design process.

Researchers who want to use co-design must be prepared for the extra time required and the need for skills concerning engagement, communication, facilitation, negotiation and resolution of conflict. Time is also required for ethical discussions and considerations concerning different types of knowledge creation.

Engaging stakeholders in co-design processes is increasingly encouraged. This study documents the key challenges in such processes and reports practical implications.

The purpose of this paper is to investigate the multidimensional nature of social and nonprofit organisations' accountability and performance measurement systems (PMSs). It further considers how these systems help in defining outcome performance indicators downward to beneficiaries

The paper discusses participatory action research (PAR) within an Italian social enterprise. In order to increase dialogue, participation and engagement, the researchers adopted focus groups as a preferred method of investigation and conducted a broad documental analysis from July 2016 to March 2018. The paper discusses the gathered data in light of the social impact value chain as well as the multiple-constituency approach.

The findings support the idea that social and nonprofit organisations lack the expertise and resources to evaluate outcomes and impact; however, through PAR, the organisation defined their desired outcomes and ascertained which internal output measures were most likely to be correlated with these outcomes. Moreover, the findings highlight that nonprofits develop outcome measurements less frequently because they have more control over their immediate activities and outputs.

This research suggests the need to reinforce lateral and downward accountability based on mission and mission-based activities in order to make the performance management system of social and nonprofit organisation linked to the organisational strategies.

This paper innovates methodologically in two directions: 1) it adopts action research as a qualitative method, allowing the researcher to generate solutions to collectively-identified problems and 2) the paper's arguments are strongly supported by rich empirical exploration that occurred over a period of 20 months in an Italian social enterprise.

The purpose of this paper is to obtain new empirical evidence about the connections between equity trading activity and five possible liquidity determinants: market capitalisation, dividend yield, earnings yield, company growth and the distinction between recently listed firms as opposed to more established ones.

The authors use a sample of 172 stocks from four European markets and estimate models using the entire sample data and different sub-samples to check the relative importance of the above determinants. The authors also conduct a factor analysis to re-classify the variables into a more succinct framework.

The evidence suggests that market capitalisation is the most important trading activity determinant, and the number of years listed ranks thereafter.

The positive relation between trading activity and market capitalisation is in line with prior literature, while the findings relating to the other determinants offer further empirical evidence which is a worthy addition in view of the contradictory results in prior research.

This study is of relevance to practitioners who would like to understand the cross-sectional variation in stock liquidity at a more detailed level.

The originality of the paper rests on two important grounds: the authors focus on trading turnover rather than on other liquidity proxies, since the former is accepted as an important determinant of the liquidity-generation process, and the authors adopt a rigorous approach towards checking the robustness of the results by considering various sub-sample configurations.