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Dr. María Teresa Ballestar
Academic Department of Economy and Finance, ESIC Business and Marketing School, 28223 Pozuelo de Alarcón, Madrid, Spain

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0 Data Analytics
0 Machine Learning
0 Electronic Commerce
0 digitalization

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Journal article
Published: 27 December 2020 in Journal of Innovation & Knowledge
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Modern economic growth is no longer found in total factor productivity (TFP) because there are gains from technological change that are never recorded in the returns from innovation or in the National Accounts. The existence of complementarities among technologies derived from the use of robotics, electronic commerce, or innovation is difficult to assess through country-level records. Because the literature has mainly focused on robotisation at an aggregate or industry level, research focusing on a firm level and complementarities analysis have been limited. To fill the gap, in this paper, we intend to provide new evidence regarding the effects of robotisation, digitisation, and innovation on productivity and employment in firms, by using a large sample of 5511 Spanish manufacturing firms for the period 1991–2016. This data captures the payoff to the high rates of investment necessary to upgrade the production technology for firms in a new globally competitive framework.

ACS Style

María Teresa Ballestar; Ester Camiña; Ángel Díaz-Chao; Joan Torrent-Sellens. Productivity and employment effects of digital complementarities. Journal of Innovation & Knowledge 2020, 6, 177 -190.

AMA Style

María Teresa Ballestar, Ester Camiña, Ángel Díaz-Chao, Joan Torrent-Sellens. Productivity and employment effects of digital complementarities. Journal of Innovation & Knowledge. 2020; 6 (3):177-190.

Chicago/Turabian Style

María Teresa Ballestar; Ester Camiña; Ángel Díaz-Chao; Joan Torrent-Sellens. 2020. "Productivity and employment effects of digital complementarities." Journal of Innovation & Knowledge 6, no. 3: 177-190.

Journal article
Published: 15 November 2020 in Oikonomics
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En este artículo se aborda el análisis del concepto de economía colaborativa, desde las diferentes corrientes del conocimiento. Así mismo, se proporciona una visión general de los diferentes modelos de negocio colaborativos que han existido hasta el momento y cómo ha sido su evolución a lo largo del tiempo debido a diferentes factores, entre los que se encuentran las tecnologías de la información y la comunicación (TIC). Si bien es cierto que estos modelos de negocio aún se encuentran en pleno proceso de consolidación, representan una gran oportunidad, tanto para usuarios que desean ver satisfechas sus necesidades de consumo, como para empresas que no solo buscan nuevas fuentes de ingreso, sino también innovación a la hora de aproximarse a sus clientes. Por último, la economía colaborativa representa un campo de investigación muy reciente y lleno de oportunidades de contribución a la ciencia y al desarrollo de nuevos modelos de negocio.

ACS Style

María Teresa Ballestar; Jorge Sainz. Modelos colaborativos de negocio en economía digital. Oikonomics 2020, 1 -9.

AMA Style

María Teresa Ballestar, Jorge Sainz. Modelos colaborativos de negocio en economía digital. Oikonomics. 2020; (14):1-9.

Chicago/Turabian Style

María Teresa Ballestar; Jorge Sainz. 2020. "Modelos colaborativos de negocio en economía digital." Oikonomics , no. 14: 1-9.

Journal article
Published: 07 October 2020 in Technological Forecasting and Social Change
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The evaluation of the impact of the adoption of industrial robotics on business is increasingly relevant in the current context of digital transformation. Although many companies are eager to adopt these technologies as a means to increase productivity, some concerns have been raised about the cost impact of the transformation, and its effect on the workforce. A growing body of literature is studying these phenomena but according to our review of it, there is no longitudinal perspective over 25 years illustrating the relationship between the attitude of companies to robotics and principal business indicators. This investigation uses an innovative machine learning model comprising an automated nested longitudinal clustering performed in two stages, and it is applied over a large sample of 4,578 companies from the Business Strategy Survey conducted by the Spanish Ministry of Finance and Public Administration. The findings of this research are novel in this field not only because of the longitudinal modelling applied in two stages but also because of the understanding of how companies’ characteristics and performance evolve over time depending on their degree of adoption of robotics. This knowledge is relevant for companies to understand the impact of their transformation to robotics. It also allows for the development of strategies that boost the efficiency of the companies, provides them with tools to protect them from negative financial events, and leads to an optimal sizing of their workforce.

ACS Style

María Teresa Ballestar; Ángel Díaz-Chao; Jorge Sainz; Joan Torrent-Sellens. Impact of robotics on manufacturing: A longitudinal machine learning perspective. Technological Forecasting and Social Change 2020, 162, 120348 .

AMA Style

María Teresa Ballestar, Ángel Díaz-Chao, Jorge Sainz, Joan Torrent-Sellens. Impact of robotics on manufacturing: A longitudinal machine learning perspective. Technological Forecasting and Social Change. 2020; 162 ():120348.

Chicago/Turabian Style

María Teresa Ballestar; Ángel Díaz-Chao; Jorge Sainz; Joan Torrent-Sellens. 2020. "Impact of robotics on manufacturing: A longitudinal machine learning perspective." Technological Forecasting and Social Change 162, no. : 120348.

Conference paper
Published: 07 May 2020 in Sustainable Transport Development, Innovation and Technology
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Political marketing strategies based on social network influencers are becoming increasingly relevant to set political agendas. We develop one of the first methods that will help us to understand the mechanism of transmission of information. We analyze the sentiment of the messages, then find and study how these messages spread over the social network of social influencers. We apply the method to one of the currently hottest topics, climate change, and to two of the most powerful social influencers, Greta Thunberg and Bill Gates, analyzing how they apply their communication strategies to reach their goals.

ACS Style

María Teresa Ballestar; Jorge Sainz. A Tale of Two Social Influencers: A New Method for the Evaluation of Social Marketing. Sustainable Transport Development, Innovation and Technology 2020, 80 -90.

AMA Style

María Teresa Ballestar, Jorge Sainz. A Tale of Two Social Influencers: A New Method for the Evaluation of Social Marketing. Sustainable Transport Development, Innovation and Technology. 2020; ():80-90.

Chicago/Turabian Style

María Teresa Ballestar; Jorge Sainz. 2020. "A Tale of Two Social Influencers: A New Method for the Evaluation of Social Marketing." Sustainable Transport Development, Innovation and Technology , no. : 80-90.

Journal article
Published: 09 March 2020 in Sustainability
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The concept of sustainability has gone far beyond the issues of the sustainable management of natural and environmental resources. Nowadays, sustainability is part of the social sciences in a different way. The aim of this research was dual. Firstly, we analyzed the different contexts and areas of knowledge where this concept is used in society by using social listening on Twitter, one of the most popular social networks today. The sentiments of these conversations were rated to assess whether the feelings and perceptions of these conversations on the social network were positive or negative regarding the use of the concept. Also, we tested if these perceptions about the topic were attuned to other more formal fields, such as scientific research, or strategies followed nationally or internationally by agencies and organizations related to sustainability. The method used on this first part of the research consisted of an analysis of 15,000 tweets collected from Twitter using natural language processing (NLP) for clustering the main areas of knowledge of topics where the concept of sustainability was used, and the sentiment of these conversations on the social network. Secondly, we mapped the social network of users who generated or spread content regarding sustainability on Twitter within the period of observation. Social network analysis (SNA) focuses on the taxonomy of the network and its dynamics and identifies the most relevant players in terms of generation of conversation and also their referrers who spread their messages worldwide. For this purpose, we used Gephi, an open source software used for network analysis and visualization, that allows for the exploration and visualization of large networks of any kind, in depth. The findings of this research are new, not only because of the mix of technology and methods used for extracting data from Twitter and analyzing them from different perspectives, but also because they show that social listening is a powerful method for analyzing relevant social phenomena. Listening on social networks can be used more effectively than other more traditional processes to gather data that are more costly and time consuming and lack the momentum and spontaneity of digital conversations.

ACS Style

María Teresa Ballestar; Miguel Cuerdo-Mir; María Teresa Freire-Rubio. The Concept of Sustainability on Social Media: A Social Listening Approach. Sustainability 2020, 12, 2122 .

AMA Style

María Teresa Ballestar, Miguel Cuerdo-Mir, María Teresa Freire-Rubio. The Concept of Sustainability on Social Media: A Social Listening Approach. Sustainability. 2020; 12 (5):2122.

Chicago/Turabian Style

María Teresa Ballestar; Miguel Cuerdo-Mir; María Teresa Freire-Rubio. 2020. "The Concept of Sustainability on Social Media: A Social Listening Approach." Sustainability 12, no. 5: 2122.

Journal article
Published: 06 November 2019 in Technological Forecasting and Social Change
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Research has become the main reference point for academic life in modern universities. Research incentives have been a controversial issue, because of the difficulty of identifying who are the main beneficiaries and what are the long-term effects. Still, new policies including financial incentives have been adopted to increase the research output at all possible levels. Little literature has been devoted to the response to those incentives. To bridge this gap, we carry out our analysis with data of a six years program developed in Madrid (Spain). Instead of using a traditional econometric approach, we design a machine learning multilevel model to discover on whom, when, and for how long those policies have an effect. The empirical model consists of an automated nested longitudinal clustering (ANLC) performed in two stages. Firstly, it performs a stratification of academics, and secondly, it performs a longitudinal segmentation for each group. The second part considers the researchers’ sociodemographic, academic information and the evolution of their performance over time in the form of the annual percentage variation of their marks over the period. The new methodology, whose robustness is tested with a multilayer perceptron artificial neural network with a back-propagation learning algorithm, shows that tenure track researchers present a better response to incentives than tenured researches, and also that gender plays an important role in academia. These discoveries are relevant to administrations and universities for understanding the productivity of academics working under long-term incentive-based programs, the drawbacks and the inequalities for maximizing the generation of knowledge.

ACS Style

María Teresa Ballestar; Luis Miguel Doncel; Jorge Sainz; Arturo Ortigosa-Blanch. A novel machine learning approach for evaluation of public policies: An application in relation to the performance of university researchers. Technological Forecasting and Social Change 2019, 149, 119756 .

AMA Style

María Teresa Ballestar, Luis Miguel Doncel, Jorge Sainz, Arturo Ortigosa-Blanch. A novel machine learning approach for evaluation of public policies: An application in relation to the performance of university researchers. Technological Forecasting and Social Change. 2019; 149 ():119756.

Chicago/Turabian Style

María Teresa Ballestar; Luis Miguel Doncel; Jorge Sainz; Arturo Ortigosa-Blanch. 2019. "A novel machine learning approach for evaluation of public policies: An application in relation to the performance of university researchers." Technological Forecasting and Social Change 149, no. : 119756.

Original paper
Published: 14 December 2018 in Review of Managerial Science
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The digital transformation of companies is having a major impact on all business areas, especially marketing, where audiences are most volatile and loyalty is at its scarcest. Many large retail brands try to keep their client base interested by becoming partners in cashback websites. These websites are based on a specific type of affiliate marketing whereby customers access a wide range of merchants and obtain financial rewards based on their activities. Besides using this mix of traditional marketing strategies, cashback websites attract new target customers and increase existing customers’ loyalty through recommendations, using a word-of-mouth marketing strategy built on economic incentives for users who refer others to these sites. The literature shows that this strategy is one of the major areas of success of this business model because customers who join following recommendation are more active and are therefore more profitable and loyal to the brand. Nevertheless, the new users who are referred to these sites vary considerably in terms of the number of transactions they make on the site. This study advances research on the design of recommendation-based digital marketing strategies by providing companies with a predictive model. This model uses data science, including machine learning methods and big data, to personalize financial incentives for users based on the quality of the new customers they refer to the cashback website. Companies can thus optimize and maximize the return on their marketing investment.

ACS Style

María Teresa Ballestar; Pilar Grau-Carles; Jorge Sainz. Predicting customer quality in e-commerce social networks: a machine learning approach. Review of Managerial Science 2018, 13, 589 -603.

AMA Style

María Teresa Ballestar, Pilar Grau-Carles, Jorge Sainz. Predicting customer quality in e-commerce social networks: a machine learning approach. Review of Managerial Science. 2018; 13 (3):589-603.

Chicago/Turabian Style

María Teresa Ballestar; Pilar Grau-Carles; Jorge Sainz. 2018. "Predicting customer quality in e-commerce social networks: a machine learning approach." Review of Managerial Science 13, no. 3: 589-603.