dataesr/scientific-paragraphs-categorization huggingface dataset:
As the construct had never been previously tested and there was no factorial structure validated for the construct, we based our factor association on statistical procedures. A Principal Components Analysis has been carried out using the Oblimin rotation method in SPSS. It revealed seven factors with eigenvalues > 1 (parameter to constitute factors). This high number, however, was not confirmed by the scree-plot analysis, which suggested three factors as being a more representative way to distribute the data, since one factor had an eigenvalue higher than 5 and two factors had values higher than 2. To support the emerging factorial structure, we have sought to adopt robust statistical procedures by carrying out an alternative Principal Components Analysis using Factor software. We also run and a Parallel Analysis with factorial retention in a polychromic correlation matrix  (Timmerman & Lorenzo-Seva, 2011).
Para a identificação dos pesquisadores atuantes em IC, em 14 de maio 2015, foi acessada a interface de "busca avançada" da PL, no campo "esta expressão booleana", utilizou-se a expressão: "(inteligencia_competitiva)". Optou-se pelos seguintes filtros: a) nas bases -doutores e demais pesquisadores (mestres, graduados, estudantes, técnicos, etc. ); b) na nacionalidade -brasileira e estrangeira; e c) país -todos. A partir da url gerada como resultado da busca, utilizouse um script (software) programado em Python para extrair os identificadores únicos (ID) de cada currículo recuperado e disponibiliza-los em um arquivo texto com a lista dos IDs de 2. 160 pesquisadores. A lista de IDs foi processada pelo software livre ScriptLattes (MENA-CHALCO; CESAR-JR, 2009) para a extração das informações dos currículos.
O resultado foi a recuperação de informações relacionadas: 1] a atuação e formação dos 2. 160 pesquisadores, contendo endereço profissional (organização e localização) e formação (áreas da graduação, especialização, mestrado e doutorado), entre outras; 2] a produção Panorama da inteligência competitiva no Brasil: os pesquisadores e a produção científica na plataforma Lattes Roniberto Morato Amaral; Aline Grasiele Cardoso Brito; karin Gomes da Silva Rocha; Luc Marie Quoniam; Leandro Innocentini Lopes de Faria científica (14. 495 referências bibliográficas de artigos completos publicados em periódicos); e 3] aos 4. 768 projetos de pesquisa, contendo ano de início e encerramento, título, descrição, situação, natureza e integrantes dos projetos. É importante ressaltar que os scripts utilizados são livres e podem ser baixados em: https://bitbucket.
org/vlab4u 1. A amostra analisada foi composta por 1. 434 currículos referentes aos indivíduos graduados, requisito mínimo para pleitear uma pósgraduação no Brasil. Com o auxílio do software VantagePoint (2015) foi selecionado o corpus principal da pesquisa em IC, formado por dados referenciais de 223 artigos que continham o termo "inteligência" em seus títulos e versavam sobre IC. Visando identificar as temáticas investigadas, foi possível recuperar os textos completos de 173 artigos, dos quais foram extraídas suas palavras-chave. A recuperação parcial dos artigos completos pode estar relacionada a não indexação desses artigos em bases de dados. Na seleção dos projetos de pesquisa que compuseram a amostra, optou-se em utilizar os que apresentavam o status "Atual" na indicação de ano de término, totalizando 747 projetos.
, 2001). A Figura 2 representa a correlação e a intensidade dos conceitos em IC investigados no Brasil, por intermédio da análise de 245 palavras-chave extraídas do texto completo de 173 artigos. Essa variedade de palavraschave, pode estar relacionada a diversidade de focos da IC (CALOF, 1999) e da pluralidade de competências necessárias ao profissional de IC  (FLEISHER, 2003; LAHEY, 2003; PRESCOTT, 2009; AMARAL et al. , 2011) , ou ainda, à interdisciplinaridade da IC  (MARCIAL; SUAIDEN, 2016). A moldura analítica aplicada à Figura 2 compreende as seguintes observações: o tamanho das palavras-chave representa o número de artigos em que a palavra-chave ocorre na amostra analisada, sua proximidade se dá pela intensidade da co-ocorrência das palavras-chave no artigo, o algoritmo estatístico Force Atlas 2 do software Gephi, tende a aproximar os nós (palavras-chave) que apresentam maior co-ocorrência e afastar o contrário.
This 36 percent response rate compares favorably to the response rates of other IJV surveys Board Representation in International Joint Ventures 929 in an Asian setting (e. g. ,  Gong et al. , 2007; Krishnan et al. , 2006; Li and Hambrick, 2005). We checked for any potential nonresponse bias by comparing available IJV and parent characteristics for respondents and nonrespondents such as IJV age and Foreign equity stake. This did not reveal any differences between respondents and nonrespondents (p > 0. 25). Similarly, as suggested by  Armstrong and Overton (1977) , we also compared early and late respondents using the covariates in our models presented below. Again this did not reveal any significant differences between these groups that might suggest any potential nonresponse bias. Most of our independent and control variables are either factual or derived from data sources other than the survey used to obtain board composition information.
Environmental volatility (Hypothesis 1) A commonly used approach to capture environmental volatility is to simply calculate the variance of some output related to the broader external environment (e. g. , stock prices, GDP, exchange rates). However, this approach does not account for predictable trends in the data and the possibility that the variance is heteroskedastic. To deal with these two concerns, we computed our environmental volatility measure using generalized autoregressive conditional heteroskedasticity (GARCH) models. More specifically, we collected daily time series data of the primary stock indices of each host country from Datastream, and we fitted GARCH models to each individual time series to get daily conditional variances for each country. Subsequently, the daily conditional variances of each country were averaged to obtain a one-year average, for the year preceding the year in which the survey was conducted.
Market growth (Hypothesis 4) We measured market growth using a seven-point Likert scale item to measure how much growth there is in the markets served by the IJV, as assessed by our respondent from the foreign IJV partner (e. g. ,  McDougall et al. , 1994). We also used an alternative measure of market growth based on stock market data to check the robustness of our findings. Specifically, we used the primary stock indices of each host country from Datastream and calculated the growth of the stock index in the year preceding the year in which the survey was conducted. While the respondents of our survey are likely to have a better understanding of what constitutes their market and how it is evolving, this more general alternative measure nevertheless yielded consistent results, providing additional statistical support for Hypothesis 4 (p = 0.
) is beyond the scope of our study, our findings in the context of IJVs highlight the relevance of looking at similar issues in corporate governance research. This further highlights the potential for fruitful cross-fertilization between the corporate governance and IJVs literatures. Finally, our findings also have implications for scholars who investigate IJV governance. Many IJV governance studies have used ownership stakes as an indicator of control, instead of more direct measures such as board representation. Although this is not surprising, since ownership data are generally more readily available, our findings suggest that this practice might be problematic, particularly under certain conditions we have identified that weaken the link between equity stakes and board representation. Therefore, we would caution against the indiscriminate use of equity ownership as an indicator of administrative control, and we would call for more research on boards or other mechanisms for administrative control in IJVs.
ACKNOWLEDGEMENTS We acknowledge the special contribution of  Dongyoub Shin, Yonsei University , who helped design the survey and collect the data. We also thank  Abhijith Acharya ,  Ruth Aguilera ,  Youtha Cuypers ,  Elko Klijn , the co-editor  Will Mitchell  and two anonymous reviewers for their insightful comments. We have also benefited from comments by participants at a presentation at  Singapore Management University. Ilya Cuypers acknowledges the financial support of  Singapore Management University  for this study (Research Grant  12-C207-SMU-001 ). An earlier version of this article won the  SMS Best Conference Paper Prize. All remaining errors are ours.
The advent of technology has allowed for the capture of large volumes of data from a variety of sources. This has led to an ever-increasing number of firms collecting large amounts of data with the belief that this will give the firm an advantage over its competitors. However, the question is, does big data by itself really lead to firm advantage? And if not, how can firms gain an advantage from big data? This paper investigates the role that big data plays in innovation and firm advantage. Using the Resource-Based View and Dynamic Capabilities framework, this paper looks at how a firm can gain an advantage from big data. Through the analysis of a case-study concerning a global information/media analytics company, this paper provides an example of how to build a capability in Digital Data Generation that can lead to improved product or service innovation, and possibly help a firm gain an advantage over its competitors.
There must be alignment between the business strategy/plans and the intentional generation of information/knowledge needed (knowledge strategy) to accomplish that strategy (knowledge strategy)  (Nelson & Winter, 1982; Dosi, Nelson, & Winter, 2000; Eaterby-Smith & Prieto, 2008). Digital Data Genesis Capability. In order to maintain its advantage, a firm must have information concerning its competitors, its internal operations, its interactions with customers, suppliers, partners, etc. It is through analysis of data that the firm understands and makes sense of what is happening in the marketplace so that it can react to changes. Data is an essential element of everyday firm performance, as well as firm advantage. A firm that runs its operations on, and makes decisions from data that is of higher quality  (DeLone & McLean, 1992)  than its competitors use, will be able to make more accurate decisions.
Therefore, data that is generated digitally will provide a firm with higher-quality data than data that is generated by other means, such as through a data-entry process. This digital data is considered to be "born digital," also referred to as Digital Data Genesis  (Piccoli & Watson, 2008; Vitari, 2009). The complementary nature of IT and organizational processes means that IT infrastructure is typically implemented throughout organizational processes and routines. And it is through these processes that IT interacts with customers, employees, suppliers, etc. (Brynjolfsson, Hitt, & Shinkyu, 2002; El Sawy & Pavlou, 2008). A capability in Digital Data Genesis is the process of choosing IT to intentionally generate/capture data digitally at the source, integrating that technology into the appropriate business processes, and then managing the data once it has been captured and stored.
The Knowledge Staircase is concerned with turning the information that was generated into knowledge and building competencies and capabilities that allow a firm to innovate and gain an advantage over its competitors. A Digital Data Genesis Capability  (Piccoli & Ives, 2005; Piccoli & Watson, 2008; Vitari, 2009)  consists of the following: 1. Choosing IT to generate and capture digital data at the source. It is important that IT personnel and consultants have current knowledge of enabling or emerging IT technologies that generate and capture data digitally. They must also be able to pair that knowledge with business issues to solve business problems. In this initial step in the Digital Data Genesis process, business management must have a thorough understanding of the business need or problem it is trying to solve, and an understanding of what the end result(s) should/will be.
2. Combining business processes and IT. In this step, the IT technology that was selected for generating and capturing digital data must be integrated into key business processes. 3. Managing digital data, which is concerned with the quality and the accessibility of the data. Data is information that can be transmitted while maintaining quality, once the syntactical rules for understanding it are known  (Shannon & Weaver, 1949; Kogut & Zander, 1992). The information capability of a firm consists of the processes and routines necessary for receiving, storing, and disseminating the digital data. Assuring and maintaining the quality of the digital data is an important component of the Digital Data Genesis IT information capability. The information repository is also an important Digital Data Genesis asset, because it allows for the use of the data that has been collected  (Piccoli & Ives, 2005).
The Outputs of a Digital Data Genesis Capability. The outputs of a Digital Data Genesis Capability (Figure1), are high-quality data that are accessible for analysis and use in decision making  (Culnan, 1985; DeLone & McLean, 2003; Gray & Meister, 2004; Hirsh & Dinkelacker, 2004; Zimmer, Henry, & Butler, 2007). Digital data quality is defined as digital data accuracy, completeness, and currency  (Nelson, Todd, & Wixom, 2005). Since information is used by business management  (Sambamurthy, Bharadwaj, & Grover, 2003)  to make business decisions and set strategy, only the highest quality, most accurate, complete, and current data will suffice. By choosing to use digital data, a firm is choosing data that is of higher quality than data obtained by other means. A ccuracy implies that the data is correct, and represents that the data that was sought to be generated is highly correlated with the data that was actually generated.
It is also an important component of accuracy that the user should experience the data as being accurate. Accuracy also implies that the data generated through a Digital Data Genesis Capability must be reliable, or the user will grow to distrust the data. Completeness refers to the data being inclusive of the information needed for its intended purpose from the user's point of view. In order for users to trust the data, it must also be perceived by them as up-to-date, current. Users will experience a great degree of frustration when analysis is performed on data that is not current, because that can lead to inaccurate conclusions being drawn and inappropriate decisions being made. Accessible data is well-located (convenient) for usage and is reliable. An example is a browser-based dashboard containing key performance indicators.
How well do the processes perform their intended function? In the case of this paper, how well does the effective implementation of a Digital Data Genesis Capability perform its function by providing data that is accessible and of high-quality so that a firm can innovate and maintain its advantage. Therefore, the outputs of a Digital Data Genesis Capability (Digital Data Accessibility and Quality) could have a positive effect upon competitive advantage. http://www. open-jim. org 101 This paper, using a published case study, "Big Data and Competitive Advantage at Nielsen"  (Prescott, 2014) , adds to the literature on the Resource-Based View and the Dynamic Capability Framework, and further investigates and operationalizes Digital Data Genesis as a capability and as a dynamic capability, and shows the role it can play in firm advantage.
Case-Study Since 1923, A. C. Nielsen  (Prescott, 2014)  has had a monopoly in the collecting and providing of data/metrics to its media and advertising industry customers concerning data about consumers' television viewing and purchasing behavior. Nielsen is the ratings engine for the advertising industry. It provides data to its customers so they can determine what decisions to make concerning programming, advertising, gaining a better understanding of what the consumer is buying, what television shows are watched, and what shows are not watched. Nielsen has maintained its monopoly by being entrenched in the way its customers do business -from what television shows get canceled or renewed for another season, to the cost of an advertising slot. Initially, Nielsen had a strong capability in data generation that was developed providing data to the radio advertising industry.
This capability was made up of certain tangible assets, such as the meters and diary that Nielsen used to collect data on radio use. It also consisted of certain intangible resources such as the routines for installing the meters in sampled homes, routines for collecting the data that was generated, etc. The meters that Nielsen used to collect data were exclusive to Nielsen; they were developed by Nielsen and they were not available for purchase in the marketplace. Once the data had been generated, it was stored, processed, and made available to Nielsen's customers for them to make decisions about advertising, etc. Therefore, there are tangible assets and intangible assets in the form of routines that make up Nielsen's capability in data generation. Since the tangible and intangible assets are exclusive to Nielsen, it would not be easy for a competitor to copy Nielsen's capability in data generation.
Therefore, isolating mechanisms/barriers to imitation should act to keep Nielsen's sustained competitive advantage from being eroded  (Rumelt, 1984; Ingemar & Cool, 1989). With the introduction of television, Nielsen adapted its capability in data generation for radio and began collecting information on television-viewing behavior. Nielsen intially used the diary method for collecting data from sample households, without the use of a meter. Not only was the diary completed manually, but the data, once collected, was manually prepared for use by Nielsen's customers. In the beginning, this data was trusted and used by Nielsen's customers to make business decisions. However, over time the use of the diary method of collecting data resulted in many complaints by the television networks and the advertising industry, as well as television viewers.
They knew that data collected using this method had the potential to contain errors, and be open to misrepresentation of what was actually being viewed by people completing the diary. This dissatisfaction was a sign of turbulence in Nielsen's business environment. In response to the dissatisfaction, Nielsen's Research and Development (R&D) function, using knowledge of the updated business plan created by Senior Mangement, developed the people meter to obtain data digitally from the television. The people meter is a set-top type box that is connected directly to the television. It captures detailed information on televsion viewing at the source, and transmits this data electronically to Nielsen's servers. The R&D function of Nielsen used their knowledge of information technology that was currently available to create the people meter.
IT was woven into the data-generation process, which was built from the routines that Nielsen used to collect data via the diary method. From the generation of data and the error-checking of data for quality, to the storing of data in data repositories, all of the routines were adapted to the meter technology. The result of the reconfiguration of the data collection process was higher-quality data that was available for use by Nielsen's customers. With the advent of broadband, people were no longer viewing television programming only on the television. They were also viewing television programming on the Internet and on their mobile devices such as cell phones. Initally, Nielsen's customers were demanding data on Internet usage. So Nielsen adapted its people meter technology to collect data on computer Internet usage.
Nielsen's customers were now getting a more complete picture of the consumer, but technology usage by the general public was changing very quickly, and Nielsen's customers were demanding more data on consumer media consumption. They now wanted data from all three screens (television, computer, and cell phone). Along with these advances in technology, new competitors were starting to appear. Cable and satellite companies, using advances in technology, were now able to collect very detailed (second-by-second) data on television viewing and Internet usage from their settop boxes installed in people's homes, and sell that data to television networks and advertisers. They were also selling their data to firms that were interested in mining these big-data sets and selling that information directly to the television networks and advertisers.
Broadband and the availability of set-top box data had caused a major shift (turbulence) in the competitive environment for Nielsen. Nielsen was no longer a monopoly, its sustained competitive advantage had been eroded, and its customers were forming relationships with its competitors. Nielsen's competitors, using big-data technologies, were now collecting data across all three screens. Even though Nielsen was not entirely caught off guard by these major changes reshaping its industry, it did not respond quickly enough because of strategic decisions made in the past. The influx of competitors had not only cost Nielsen its sustained competitive advantage, but Nielsen was in debt and ended up being sold to a group of investors, who hired David Calhoun as the CEO. Prior to Calhoun taking over, Nielsen's business units were functioning independently.
Therefore, even though business intelligence data was being collected according to Nielsen's knowledge strategy, it was not being shared across the business units, causing an incomplete picture of the competitive environment. Nielsen did not respond to the advances in technology or set-top box data in a timely manner. Nielsen was not in immediate danger of going out of business. It still was the major provider of data to the network television and advertising industry. Due to the relationships with its customers which had been built up over time, and its capability in needs of its customers by providing a more complete picture of the consumer ahead of its competitors. Nielsen was not only providing a more complete picture of the consumer, it was also providing information which was of high-quality, and accessible to its customers for decision making.
Digital Data Genesis Capability Using the Nielsen case  (Prescott, 2014) , this research sought to operationalize Digital Data Genesis Capability. Senior management makes decisions based upon data. Therefore, data is a critical organizational asset  (Huber & Power, 1983). Those decisions impact the resource allocation and the business strategy of the firm, and the competitive advantage and even survival of the firm. It is absolutely imperative that decision makers have access to high-quality data on which to base their decisions. It is possible that gaining knowledge or making decisions from information that is not of high quality can result in the wrong decisions being made  (DeLone & McLean, 1992). As a company advances sequentially along the Knowledge Staircase, it develops a capability in using knowledge to innovate.