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Prior research on business analytics has advanced substantially our understanding of how social media analytics affect business performance. However, the specific value of social media analytics to product innovation has not been fully explored and appreciated. To address this important issue, the present study draws on the resource-based view and the knowledge-based view to examine (1) whether the use of social media analytics strengthens radical product innovation to a greater extent than it does incremental product innovation and (2) how knowledge-exploration competence and knowledge-exploitation competence mediate the influence of social media analytics on radical and incremental product innovation.

This study tested the proposed model using data collected from 205 manufacturing firms. Structural equation modeling was applied to test the research hypotheses using LISREL 8.80 software program.

The statistical findings provide compelling evidence that the use of social media analytics is more likely to lead to radical product innovation than to incremental product innovation. In addition, knowledge-exploration competence only partially mediates the relationship between social media analytics and radical product innovation. Knowledge-exploitation competence not only partially mediates such a relationship, but also fully mediates the link between social media analytics and incremental product innovation.

This study contributes to the social media analytics and innovation literature by offering novel theoretical and empirical insights into how firms can leverage the value of social media analytics to create superior product innovation.

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 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.

To unlock social media’s value, this study aims to integrate insights from several theoretical perspectives and the relevant literature, developing an extended social media analytics framework. It identifies the stages underlying the social media analytics process and tests the framework in three important and interconnected areas: social media (Twitter), new product adoption (iWatch and Google Glass) and social media analytic techniques (text mining and sentiment analysis).

Based upon a systematic review of different research approaches, theories and media types, this paper presents and tests an extended framework in three important and interconnected areas mentioned above.

This paper offers a theory-driven social media analytics framework. It validates the framework by providing concrete processes, examples, evidence and insights related to three chosen areas mentioned above, thereby helping managers create effective and efficient social media and new product development strategies.

This paper integrates insights from theories of the middle range (Merton, 1949), Campbell’s (1965) model of sociocultural evolution and Fan and Gordon’s (2014) social media analytics framework, developing its own extended social media analytics framework and validating it in three important and interconnected areas mentioned above. This paper demonstrates not only how the proposed framework can be applied to the context of new product development, but also how social media are transforming research approaches (qualitative, quantitative and mixed method) and the very nature of business itself (increased importance of digital business).

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 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.

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 study aims to investigate the time for social media posts and reviews in order to determine the best timing to ensure maximum outreach and interactions from users. The study intends to analytically investigate a company's Facebook and Instagram pages to get meaningful insights for effective post management.

“Great Deal Tires” company’s Facebook and Instagram business pages were taken as the case study and patterns and analytical insights for different posts using Facebook and Instagram analytics were identified. The study categorically selected posts from Facebook pages and Instagram pages that were posted at different times and assessed on their impact.

According to the study, Tuesday and Wednesday have higher engagement on the Great Deal Tires Facebook page, while Friday has higher engagement on Instagram. The study also provided valuable insights into post content and timing in order to increase the marketing impact of the posts.

The study provides an analytical framework for analyzing post and review timing on various company business pages, allowing marketers to initiate more user visits and interactions.

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.