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The present research aimed to identify the motivations, needs, wants, preferences and limitations of corporate professionals with regard to business social analytics.

Online interviews were conducted with 26 professionals the majority of whom work at the management level at 20 reputable corporations in Turkey. Both qualitative and quantitative data was collected during these interviews, which lasted an average of one hour.

The findings shed light on the motivations of corporate professionals for monitoring social media and other digital media, their perceived capability and limitations in doing so, the media that they monitor and wanted to monitor if possible, their criteria and processes for working with service providers in the field of business social analytics, their needs which are not fully met by service providers, their suggestions on service improvement and their reflections on how internal and external customer data can be analyzed with an integrated approach.

This research is an attempt to bridge the gap between the priorities of engineers who generate artificial intelligence for the purposes of social listening and analytics and the end users, e.g. corporate communication professionals. Only by doing so, this field, which is getting more and more important as people spend more time online, will reach its full potential and benefit corporations by providing fruitful insight upon which strategic steps can be taken.

The purpose of this study was to analyze the effects of social media analytics on firm performance using a sample of small and medium enterprises (SMEs) in Thailand. This study also investigated whether entrepreneurial orientation (EO) moderated the effects of social media analytics on firm performance.

This study used SMEs listed in the Department of Business Development of Thailand as the sampling frame. Probability sampling was used to draw the sample. A questionnaire survey was used to collect data from 334 firms. The data were analyzed using partial least squares structural equation modeling.

The results supported the positive association between social media analytics practices on firm performance. Moreover, this study found that EO moderated this association significantly. In particular, the positive association between social media analytics practices on firm performance was higher for firms that exhibit a high EO than those that exhibit a low EO. This result indicated that firms that implement social media analytics practices achieved higher performance when they exhibited a high EO.

Social media data analytics should be implemented to strengthen the technological competence of firms. Moreover, firms should integrate EO practices into their implementation of social media analytics to increase their ability to generate substantial improvements in their strategic implementation, thereby enabling them to gain sustainable competitiveness in their market.

Because SMEs are the driving force for economic growth and development in Thailand, their ability to achieve higher performance when they effectively integrate EO practices into their implementation of social media data analytics could be beneficial for the sustainable development of Thailand, especially in the current data-driven era.

The result that EO moderates the effect in enhancing social media analytics practices’ influence on firm performance provides new knowledge that extends the boundary of research on this topic. The authors provided a theoretical explanation to clarify the way the implementation of social media analytics practices should be integrated with EO to increase the level of performance that firms achieve from such practices.

The purpose of this paper is to provide a review of hospitality and tourism studies that have used social media analytics to collect, examine, summarize and interpret “big data” derived from social media. It proposes improved approaches by documenting past and current analytic practice addressed by the selected studies in social media analytics.

Studies from the past 18 years were identified and collected from five international electronic bibliographic databases. Social media analytics-related terms and keywords in the titles, keywords or abstracts were used to identify relevant articles. Book chapters, conference papers and articles not written in English were excluded from analysis. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) guided the search, and Stieglitz and Dang-Xuan’s (2013) social media analytics framework was adapted to categorize methods reported in each article.

The research purpose of each study was identified and categorized to better understand the questions social media analytics were being used to address, as well as the frequency of each method’s use. Since 2014, rapid growth of social media analytics was observed, along with an expanded use of multiple analytic methods, including accuracy testing. These factors suggest an increased commitment to and competency in conducting comprehensive and robust social media data analyses. Improved use of methods such as social network analysis, comparative analysis and trend analysis is recommended. Consumer-review networks and social networking sites were the main social media platforms from which data were gathered; simultaneous analysis of multi-platform/sources of data is recommended to improve validity and comprehensive understanding.

This is the first systematic literature review of the application of social media analytics in hospitality and tourism research. The study highlights advancements in social media analytics and recommends an expansion of approaches; common analytical methods such as text analysis and sentiment analysis should be supplemented by infrequently used approaches such as comparative analysis and spatial analysis.

本文对酒店旅游学科中采用社交媒体数据分析的文献进行梳理。本文通过审阅其相关分析方法的文献来提出分析方法的改进策略。

样本数据包括过去18年中五个国际在线文献索引库中的文献。搜索通过标题、关键词、或者摘要中出现社交媒体数据分析等相关字样的文章。书章节、会议文章、以及非英文文章未被收录在索引中。系统回顾和文献综述的方法（PRISMA）指导本文文献索引, Stieglitz和Dang-Xuan（2013）社交媒体数据分析框架作为本文文献分类的方法。

本文汇报了每篇文献的研究目的以及系统归类以更好理解社交媒体数据分析的研究问题以及每种方法的使用频率。自2014年起, 社交媒体数据分析快速增长, 以及其他相关分析方法, 包括精度测试（accuracy testing）。这些结果表明更多全面、稳定的分析方法需求增强以及竞争激烈。本文推荐使用改良方法, 比如社交网络分析法、比较分析、趋势分析等。消费者评价网络和社交网站成为主要社交媒体网络数据的提供平台。本文推荐多源数据应该同步分析以提高有效性和全面性的理解。

本文是首篇酒店旅游领域中对社交媒体数据分析的系统文献回顾型文章。本文强调了社交媒体数据分析的先进性以及扩展其方法的全面性；常见分析方法比如文本分析和情感分析应该结合非常见的分析方法比如比较分析法和空间分析法进行系统分析。

关键词 – 关键词 对比分析, 情感分析, 用户原创内容,社交媒体分析, 主题模型, 空间分析, 文本分析文章类型 文献综述

The purpose of this study is to investigate the impact of corporate social responsibility (CSR) on innovation performance and examine the moderating role of social media strategic capability and big data analytics capability. Specifically, the authors explore the effects of both external and internal CSR on innovation performance.

The authors collected data from 221 senior, middle and research and development (R&D) managers of high-tech firms in China, using a questionnaire survey with a six-month interval.

The empirical results show that both external and internal CSR positively influence innovation performance. Furthermore, social media strategic capability has a positive moderating effect on the relationship between CSR and innovation performance, while big data analytics capability moderates the relationship between external CSR and innovation performance.

The data comes from high-tech firms in China, which may limit the generalizability and external validity of the findings. Future studies should replicate this study in other industries and types of organizations.

The study suggests that high-tech firms should engage in both external and internal CSR activities to promote innovation performance. Moreover, leveraging social media strategic capability and big data analytics capability can enhance innovation performance.

This study contributes to the literature on CSR outcomes by empirically exploring the effects of external and internal CSR on innovation performance, thus extending stakeholder theory. Additionally, by revealing the contingency effects of social media strategic capability and big data analytics capability, this study enriching the research on dynamic capabilities theory in the context of digital transformation.

The outbreak of COVID-19 is one of the most serious health events in recent times. In the business landscape, its effects may be more detrimental to micro-, small- and medium-sized enterprises (MSMEs) because they tend to have limited financial and human resources to manage the challenges caused by COVID-19. To help MSMEs enhance their resilience, this paper aims to discuss how they can leverage mass collaboration to build social media-based knowledge ecosystems to manage interactions among internal and external stakeholders for knowledge creation and innovation.

The paper proposes a model for MSMEs to build an online knowledge ecosystem and a standalone text analytics tool to use the advanced data analytics, e.g. topic modeling, to analyze and aggregate collective insights. Design science research methodology is used to develop the model and the tool.

Through mass collaboration using social media and advanced data analytics technology, MSMEs can generate new business ideas, leading to enhanced resilience to meet the challenges caused by COVID-19 or other unexpected or extraordinary circumstances, such as natural disasters and financial crises.

To the best of authors’ knowledge, this paper is one of the first papers in social media adoption for knowledge creation and innovation research, providing detailed approaches for MSMEs to build a knowledge ecosystem on social media and to use advanced data analytics to mine the meaning of the generated data.

As faculty professional development increasingly occurs online and through social media, it becomes challenging to assess the quality of learning and effectiveness of programs and resources, yet it is important to evaluate such initiatives. The purpose of this paper is to explore how one faculty development center experimented with using analytics to answer questions about the use and effectiveness of its web and social media resources.

The case study was based on direct observation of the center’s practice and review of selected data generated by the analytic tools.

Unfortunately, while some analytics are available from a variety of sources, they are often distributed across tools and services. The center developed an analytics strategy to use data from Google Analytics and social media reporting tools to assess the use of online and social professional development resources. Initial results show that the center’s online and social professional development resources are widely used, both within and outside the university. However, more work is necessary to improve the strength and scope of the available analytics.

As a result of the analysis, the center has streamlined online resources, targeted social media use, and has begun developing methods to allow faculty to report online resource use as professional development for academic personnel purposes.

Many faculty development centers have not explored methods of evaluating online and social media resources. This paper outlines a strategic evaluation plan to measure the usage of online resources as well as engagement and interaction through social media.

Research on social media frequently analyze social media usage (SMU)'s positive consequences for organizations and individuals; however, recent innovation studies caution that SMU may not always lead to positive new product development (NPD) outcomes. The competing streams of research highlight a fundamental tension that exists in the social media literature exemplified by the question: Is SMU good or bad for NPD? In this manuscript, the authors suggest that a more appropriate question as follows: What are the positive and negative indirect effects of SMU on NPD performance? The purpose of this paper is to discuss the aforementioned points in detail.

A literature review provides the model and hypotheses. Using a sample of 168 Chinese firms, the authors conducted an empirical test following multiple regression analysis.

The results demonstrate that SMU facilitates business analytics (ability) and social legitimacy (opportunity) but impairs entrepreneurial proclivity (motivation). These three constructs in turn mediate the effect of SMU on NPD performance. Moreover, this paper explores how technological turbulence moderates SMU's effects on business analytics, entrepreneurship proclivity and social legitimacy.

The results may be affected by both the context (solely in China) and type (cross-sectional) of the data set. Future research might take a decompositional approach to study SMU's effect on innovation in different NPD stages. Furthermore, with widely varying purposes (e.g., marketing, information searching, partner collaboration, new product launch, etc.), there is certainly a need for more clarity and understanding of how firms can leverage each of these different social media activities for successful NPD.

First, we suggest that managers in China should be explicitly aware of the double-edged sword effect of SMU on NPD performance. Second, this study encourages managers to use social media carefully when technological turbulence becomes intense.

Drawing on the ability–motivation–opportunity framework, this is one of the first studies to simultaneously examines the benefits and costs of SMU for NPD. In addition, this paper bridges the separate literatures on social media, business analytics, entrepreneurial proclivity and social legitimacy and contributes to the NPD research.

The aim of this study was to understand how public relations leaders view and use social media analytics (SMA) and the impact of SMA on the public relations function.

The research involved in-depth interviews with chief communication officers (CCOs) from leading multinational corporate brands.

The findings revealed that although CCOs perceive social media analytics as strategically important to the advancement of public relations, the use of social media data is slowed by challenges associated with building SMA capacity.

The research extends public relations theory on public relations as a strategic management function and provides practical insights for building SMA capabilities.

The study is among the first to provide empirical evidence of how companies are using social media analytics to enhance public relations efforts.

The purpose of this article is to provide an interview with the former web analytics guru, Marshall Sponder.

The paper provides an interview with Marshall Sponder, author of Social Media Analytics.

This special issue concludes with an interview with web analytics guru, Marshall Sponder. For over a decade, Marshall has been at the forefront of this growing field, influencing and advising large companies on how to determine ROI from their web‐based activity. His book, Social Media Analytics, proffers strategies for getting the most out of social media, as well as predicting the direction this field is likely to take in future, something Sponder is perfectly placed to do. In this interview he discusses at length the importance of a tactical approach to social media, the tools needed for measuring ROI, and emerging trends in social media.

The interview provides an insightful look at the emerging trends in social media analytics from the leading analyst in the field.

This chapter reviews the growth of data analytics in sport by taking a social media perspective. There has been an emphasis on the use of real-time statistics by sport organizations in order to increase their performance in the marketplace. This has been made possible by increased emphasis on technological innovations that enable data analytics to be conducted. The current developments and areas of application to the sport industry are stated and prospects for future advancement noted. This includes a focus on emerging trends in artificial intelligence and social media marketing. A concluding section focusing on the implications of current research and practice stresses the need for more novel approaches to be used.