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Explore the use of Gender-Fair Language (GFL) by influencers on Instagram.

The clustering methodology. A digital Bag-of-Words (BoW) Method called GFL Clustering BoW Methodology to identify whether an inclusive marketing (IM) strategy can be used. Thus, this research has a methodological and practical contribution to increasing the number of marketing technology tools.

This study is original as it proposes an inclusive digital marketing strategy and contributes with methods associated with digital transfers in order to improve marketing strategies, tactics and operations for inclusive content with a data integrity approach.

Due to the limitations of the application programming interface (API) of the social network Instagram, a limited number of text data were used, which allowed for retrieving the last 12 publications of each studied profile. In addition, it should be considered that this study only includes the Spanish language and is applied to a sample of influencers from Chile.

The practical contribution of this study will lead to a key finding for the definition of communication strategies in both public and private organizations.

The originality of this work lies in its attractive implications for nonprofit and for-profit organizations, government bodies and private enterprises in the measurement of the success of campaigns with an IM communicational strategy and to incorporate inclusive and non-sexist content for their consumers so as to contribute to society.

本研究擬探究有影響力的人士在使用即時電報 (Instagram) 時、如何使用性別中立語言。

研究使用了聚類分析法;具體來說, 研究人員採用一個叫 GFL聚類詞袋法的數位詞袋分析法, 去確定研究可否使用信息管理策略。因此, 本研究在行銷科技方面、添加了一個工具, 就此而言, 本研究在學術的研究法和實務方面、均作出貢獻。

本研究建議了一個包括一切的數位行銷策略;研究亦構建了若干與數位傳輸有關的方法, 以能利用數據完整性的理念, 為行銷策略、行銷戰術和市場營銷, 在內容的全面包含度方面取得改善。

因為社交網站即時電報的應用程式介面有其局限, 故使用了少量的文本數據, 這可使每個被探討的傳略的最後12個發佈能被撿回。另外需注意的是、本研究只涵蓋西班牙語, 而且, 研究使用的樣本只是來自智利有影響力的人士。

本研究在實務方面的貢獻是、它為探討在公共機構和私營機構內使用的溝通策略的定義上、帶來重要的啟發和發現。

本研究的原創性在於它給營利和非營利組織、政府機關和私人企業帶來頗具吸引力的啟示。而這些啟示是與測量以包括一切的行銷溝通策略進行的專門活動是否成功有關的。另外, 涵蓋一切和無性別歧視的內容被納入供消費者使用, 以此為社會帶來裨益。

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

The technical feasibility of using Benford's law to assist internal auditors in reviewing the integrity of high-volume data sets is analysed. This study explores whether Benford's distribution applies to the set of numbers represented by the quantity of records (size) that comprise the different tables that make up a state-owned enterprise's (SOE) enterprise resource planning (ERP) relational database. The use of Benford's law streamlines the search for possible abnormalities within the ERP system's data set, increasing the ability of the internal audit functions (IAFs) to detect anomalies within the database. In the SOEs of emerging economies, where groups compete for power and resources, internal auditors are better off employing analytical tests to discharge their duties without getting involved in power struggles.

Records of eight databases of an SOE in Argentina are used to analyse the number of records of each table in periods of three to 12 years. The case develops step-by-step Benford's law application to test each ERP module records using Chi-squared (χ²) and mean absolute deviation (MAD) goodness-of-fit tests.

Benford's law is an adequate tool for performing integrity tests of high-volume databases. A minimum of 350 tables within each database are required for the MAD test to be effective; this threshold is higher than the 67 reported by earlier researches. Robust results are obtained for the complete ERP system and for large modules; modules with less than 350 tables show low conformity with Benford's law.

This study is not about detecting fraud; it aims to help internal auditors red flag databases that will need further attention, making the most out of available limited resources in SOEs. The contribution is a simple, cheap and useful quantitative tool that can be employed by internal auditors in emerging economies to perform the first scan of the data contained in relational databases.

This paper provides a tool to test whether large amounts of data behave as expected, and if not, they can be pinpointed for future investigation. It offers tests and explanations on the tool's application so that internal auditors of SOEs in emerging economies can use it, particularly those that face divergent expectations from antagonist powerful interest groups.

This study demonstrates that even in the context of limited information technology tools available for internal auditors, there are simple and inexpensive tests to review the integrity of high-volume databases. It also extends the literature on high-volume database integrity tests and our knowledge of the IAF in Civil law countries, particularly emerging economies in Latin America.

Communication and job satisfaction are two key factors for the success of public organisations. The study aims, firstly, to examine the effects of training activities and internal meetings on the perceived quality of communication processes; secondly, to explore the nuanced relationship between communication and job satisfaction.

This is a cross-sectional study based on results from organisational climate surveys conducted between 2019 and 2020 in six Italian Regional Healthcare Systems, involving a total of 58,888 employees. Linear regression models were performed.

Results highlight that employee satisfaction is positively influenced by high-quality internal communication. The organisation of frequent internal meetings and the offer of ad hoc training sessions on communication were found to be antecedents of good communication.

The study offers practical implications for healthcare organisations, highlighting specific strategies that can be employed to enhance internal communication and, subsequently, job satisfaction. The emphasis on training programmes and internal meetings provides actionable insights for organisational improvement.

The originality and value of this study lie in its unique large datasets, which significantly strengthen the reliability of the findings and make them more applicable to a wider range of contexts. Also, the exploration of the relationship between internal communication processes and job satisfaction within public healthcare organisations contributes to the growing body of knowledge in organisational studies.

As subjects irrationally perceive probability changes as more impactful when shifting an event from impossible to possible or from possible to certain, compared to increasing the likelihood of an already possible event, this study examines how workers process success probabilities and whether their resource allocation decisions are distorted by bounded subadditivity.

We conduct an online randomized experiment with 3,980 employees.

We detect a certainty effect (upper subadditivity), whereby professionals are willing to devote a disproportionate number of hours to a project when their contribution transforms the success of the initiative from possible to certain rather than increasing the likelihood of success by the same percentage points. We find no evidence of the possibility effect (lower subadditivity), whereby workers would devote a disproportionate effort when their contribution turns a sure failure into a possible success rather than simply increasing the likelihood of success by the same percentage points. We observe a rational tendency to try harder for a greater increase in the probability of success, but only far from the limits of the probability spectrum and not close to the limits.

Attempts to understand bounded subadditivity in management decisions have been incomplete. We disentangle two real-world variables and offer a more refined operationalization.

This study aims to explore the role of rebel group legitimacy and ideology in durable peace (DP) following peace agreements to end civil wars. It builds on earlier research showing that justice and civil society involvement are critical in achieving DP. This study adds the impacts of rebel group activities and support on DP. Activities include service delivery and mobilization. Support is gauged with outcomes of presidential and parliamentary elections held following peace agreements.

Five data sets were used to measure the key variables: DP, inclusive commissions (IC), legitimacy symmetry (electoral outcomes), service delivery and ideological mobilization. A measure of rebel group integration in the political system was also constructed. Impacts of the integration, legitimacy and ideology variables were assessed with a hierarchical regression model (HRM). This study begins with a base model drawn from earlier research showing the key predictors were procedural justice (PJ) and IC. The authors ask about the extent to which the rebel group variables contribute additional variance to the prediction of DP.

The main contributors to the prediction of DP were PJ, IC and integration in the political system. None of the legitimacy or mobilization variables added significant variance to the prediction. Only one of the mobilization variables, forced recruitment, was significant. The decision to integrate into the political system following the agreement did not mediate the relationship between PJ in the negotiation process and DP. Results of a factor analysis showed that DP, PJ, IC and integration formed a cluster with strong loadings on the first factor.

The negative results for the legitimacy and mobilization variables may not be the last word on rebel group influences. Lack of support for the key hypotheses spurs attempts to discover other sources that contribute to the survival of rebel group actors in the political system and, in turn, to DP.

The issues raised by this study contribute to debates about ways to attain peaceful relations among competing groups following a civil war. It appears that attention to factors inside and around the negotiation process (PJ, ICs and conversion) may be more important than rebel group activities outside of these processes. The results call attention, in particular, to the important role played by political integration. From a policy perspective, it would be useful to develop levers for encouraging rebel groups to emerge as political actors in the post-agreement environment.

Developing measures of the symmetry of rebel group legitimacy and integration in the context of a comparative case study are the primary original contributions of this study. Furthermore, the mode of analysis (HRM) is novel in this literature. This approach builds on and extends the earlier research on factors influencing DP.

The purpose of this paper is to present an overview of the main messages and key questions for further research arising from the seven-seminar series entitled, “Innovative technologies for autism: critical reflections on digital bubbles”, funded by the Economic and Social Research Council in the UK.

A synthesis of the main ideas is presented, drawing on the presentations, discussions, participant feedback, and short papers from across the seminar series, which took place between November 2014-2016.

There were many positive examples where technologies were positioned and used as facilitative “bridges” between ideas, communities, understanding, and experiences. Researchers and community stakeholders also emphasised the importance of taking different perspectives and working in stronger partnerships with each other. Four overarching research questions were developed from these themes to provide a roadmap for future research, relating to: responsible innovation, technology-enabled social interaction, learning and pedagogy, and engagement.

The findings and methodologies produced by the Digital Bubbles seminar series, available on the project website (http://digitalbubbles.org.uk/) and in a series of short papers, provide a rich repository of state-of-the-art thinking in the field of autism and technology that is being utilised nationally and internationally in teaching and learning. This paper suggests some valuable future research directions and highlights the importance of establishing and maintaining multi-disciplinary research teams, with autistic people and their families at their core.

How to design jobs to support innovation is an issue that has received plenty of consideration over the past years. Building on the job characteristics model, the present study is set up to identify configurations of perceived job characteristics for innovation.

By adopting a fuzzy-set configurational approach (fsQCA), the research question is addressed through a two-wave self-report survey of 199 employees of an Italian manufacturing company.

Results reveal four compatible configurations of job characteristics leading to high levels of innovative work behavior and two for low levels.

The results offer guidance for managers and organizations that aim to strengthen employee-driven innovation by offering different recipes of job design to maximize the chance of boosting innovative behaviors among their workers.

This research is one of the first to empirically test the relation of job characteristics for innovative behavior using a configurational approach. By doing so it contributes to the literature by advancing the notion that innovative endeavors are determined by the holistic effects of different interdependent configurations of job characteristics.

This study aims to explore the potential of social enterprises (SEs) in promoting sustainable practices, focusing on their role in reshaping corporate environmental performance (CEP) through sustainable business model innovation (SBMI). Specifically, it examines the impact of SBMI on CEP and the moderating effect of external collaboration (EC).

This study analyses the influence of SBMI on the CEP of 500 Canadian SEs. Chi-square tests, structural equation modelling, correlation analysis and regression analysis were used to assess the relationships between SBMI, CEP and EC.

Results reveal that SBMI positively influences CEP by enabling SEs to offer environmentally sustainable products and services. In addition, collaboration with diverse stakeholders significantly enhances the effectiveness of SBMI in achieving environmental objectives.

By incorporating ecological modernization theory and institutional theory, this study provides fresh insights into the environmental impact of SEs. It underscores the importance of SEs addressing regulatory, social and cultural factors to support their sustainability and legitimacy.