Abstract
Data Science is a tool for organizations to accelerate the development of innovative solutions through collaboration among Small and Medium Enterprises (SMEs), leading to the Industry 4.0 approach. Data is essential in decision-making. Many organizations can't access timely relevant information because they don't own it or there is a lack of collaboration among third parties. Often, data-sharing and collaborative approaches can benefit both, increase the market they operate in, and accelerate innovation. This paper identifies and analyzes the current utility of Data Science for Industry 4.0 from an Open Design perspective to accelerate innovation across different industries. © 2022 The Author(s).

Abstract
This paper aims to contribute to the conceptual debate on the connection between social innovation and social entrepreneurship, considering the role of the ‘social’ within this connection. Supported by a systematic literature review (SLR) with an in-depth analysis of 34 articles from Scopus-indexed and Web of Science databases journals, this paper identifies, analyzes and describes the difficulties and opportunities in the social innovation and social entrepreneurship literature. Little is known about the link between both concepts and the influence of the ‘social’ in their development and implementation. This SLR was conducted to identify and describe definitions and patterns. Results show that the connection between social innovation and social entrepreneurship is in its take-off phase, but it still is a fragmented field with a huge lack of consensus. Thus, it will be important to see where the field will head, as this paper aims to be a first step in the understanding of social innovation and social entrepreneurship through a collective and integrated perspective, providing an elucidation of the different perspectives of the literature. © 2022 China Ordnance Society

Abstract
The purpose of this article is to identify the most impactful research on customer churn and to map the conceptual and intellectual structure of its field of study. Data were collected from the WoS database, comprising 338 articles published between 1995 and 2020. Several bibliometric techniques were applied, including analysis of co-words, co-citation, bibliographic coupling, and co-authorship networks. R software and the Bibliometrix/Biblioshiny package were used to perform the analyses. The results identify the most active and influential authors, articles, and journals on the topic. More specifically, through co-citations and bibliographic coupling, it was possible to map the oldest articles (retrospective analysis) and the current research front (prospective analysis). The retrospective analysis, based on co-citations, revealed that the foundations of this research field are constructs such as quality of service, satisfaction, loyalty, and changing behaviors. The prospective analysis, performed through bibliographic coupling, revealed that current research is embedded in predictive analysis, clusters, data mining, and algorithms. The results provide robust guidance for further investigation in this field.

Abstract
There is an increasing number of academic publications on studying the impact of the gig economy and digital platforms. Some of them involve entrepreneurship and business models. However, there is a lack of a global picture depicting the scientific structure of knowledge regarding the gig economy and entrepreneurship. This paper presents a conceptual, intellectual, and social bibliometric overview, using Bibliometrix and Biblioshiny (R-packages). To this end, total of 345 published articles were analyzed, covering 245 sources, 44 countries and 751 authors. There are several important findings: five main clusters emerged from the study (Self-employment and social economy; Sharing economy and sustainable development; Entrepreneurship and innovation; Gig economy and platform economy; and Digitalization); the main themes that emerge deal with sharing, gig, and platform economy, digitalization, teleworking, career participation and platforms; finally, gig workers are key for developing strategies, policies, and actions to achieve a social welfare through entrepreneurship in the platform ecosystem. It is also important to highlight the role of communities and social capital in the development of sustainable collaborative initiatives through digital entrepreneurship.

Abstract
The objective of this article is to analyze and empirically validate the differential effects in the daily schedules of the induced electricity prices by selling bids for three different technologies, namely hydraulic, thermal and renewable energy sources (RES), in hourly values, by daily observations for the year 2018. To achieve this objective, we employ an autoregressive distributed lag (ARDL) model-bound testing approach The results of the ADRL-ECM method, which also reports the long-run analysis, show that (a) the renewable and thermal technologies positively and significantly affect the electricity price for Endesa and Hidroeletrica del Cantabrico generators and (b) the hydraulic technology impacts negatively the electricity price, both at a 1% level of significance. In addition, following a long-term perspective it must be highlighted that RES negatively impact the price of electricity with a 1% level of significance for the Iberdrola, E.ON Energy, Union Fenosa and EDP Energy of Portugal generators. However based on a short-term perspective, the results report a positive effect between the quantities traded by hydraulic and thermal technologies on the electricity price for Endesa, Iberdrola, Hidroeletrica del Cantabrico and EDP Energy of Portugal, at a 1% level of significance. (C) 2022 The Author(s). Published by Elsevier Ltd.

Abstract
This article aims is to address whether, and to what extent, socioeconomic conditions influence entrepreneurship-based activities in 18 European countries grouped into subregions (North, South, East and West) during the period 2008-2018. We conducted the empirical study under a two-stage DEA model. The results of scores of technical efficiency of the first stage of DEA show inefficiency in Northern Europe for Finland, Ireland and Sweden, while, Belgium, Germany, France and Luxembourg are also inefficient countries in Western Europe. Finally, Spain and Italy are inefficient in Southern European group of countries. In the second stage of DEA, through the parametric fractional regression model (FRM), which consolidated results of one-part and two-part models, the results show that the Expenditure on Social Protection produces positive (high magnitude coefficient) statistical significance on TEA of Eastern, Western and Southern EU countries, while generates a negative (high magnitude coefficient) statistical significance on TEA of Northern EU countries. Moreover, in the Eastern, Western and Southern EU countries, in contrast with Northern EU countries, presumably the dimensions of the social welfare (employment/unemployment protection and so on) are used to encourage individuals to invest in specific skills and/or new businesses. Furthermore, the results in this second stage of DEA helps gain a thorough perspective on how economic and social impacts result from TEA efficiency.