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This study aims to predict popular contributors through text representations of user-generated content in open crowds.

Three text representation approaches – count vector, Tf-Idf vector, word embedding and supervised machine learning techniques – are used to generate popular contributor predictions.

The results of the experiments demonstrate that popular contributor predictions are considered successful. The F1 scores are all higher than the baseline model. Popular contributors in open crowds can be predicted through user-generated content.

This research presents brand new empirical evidence drawn from text representations of user-generated content that reveals why some contributors' ideas are more viral than others in open crowds.

This research suggests that companies can learn from popular contributors in ways that help them improve customer agility and better satisfy customers' needs. In addition to boosting customer engagement and triggering discussion, popular contributors' ideas provide insights into the latest trends and customer preferences. The results of this study will benefit marketing strategy, new product development, customer agility and management of information systems.

The paper provides new empirical evidence for popular contributor prediction in an innovation crowd through text representation approaches.

The lack of clarity in defining data science is problematic in both academia and industry because the former has a need for clarity to establish curriculum guidelines in their work to prepare future professionals, and the latter has a need for information to establish clear job description guidelines to recruit professionals. This lack of clarity has resulted in job descriptions with significant overlap among different related professional groups. This study examines the industry view of five professions: statistical analysts (SAs), big data analytics professionals (BDAs), data scientists (DSs), data analysts (DAs) and business analytics professionals (BAs). The study compares the five fields with the unified backdrop of their common semantic dimensions and examines their recent dynamics.

1,200 job descriptions for the five Big Data professions (SA, DS, BDA, DA and BA) were pulled from the Monster website at four points in time, and a document library was created. The collected job qualification records were analyzed using the text analytic method of Latent Semantic Analysis (LSAs), which extract topics based on observed text usage patterns.

The findings indicated a good alignment between the industry view and the academic view of data science as a blend of statistical and programming skills. This industry view remained relatively stable during the 4 years of our study period.

This research paper builds upon a long tradition of related studies and commentaries. Rather than relying on subjective expertise, this study examined the job market and used text analytics to discern a space of skill and qualification dimensions from job announcements related to five big data professions.

The revitalization of big data has gained attention in the public sector. However, such open government data (OGD) is facing major challenges with respect to data quality and limited use. To solve this problem, this study analyzes the factors driving the use of OGD from the perspective of data providers in the public sector.

Using the analytic hierarchy process and analytic network process methodologies, the importance of the factors driving the use of big data in the public sector was ranked. In addition, the different characteristics of tasks among the departments in a public agency were compared based on expert interviews.

The factors driving OGD use are not only political environment or the technological environment. The importance of the institutional culture within the organization increases with the motivation of the data provider. The priorities of the OGD factors also depend on the objectives of the department involved.

This study provides implications for improving the publication of open data by analyzing the priorities of the factors driving its use from the perspective of big data providers. It focuses on different perceptions of the factors valued by public officials in charge of data in institutions. The results suggest the need to explore officials' perceptions of value creation in big data fields.