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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 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.

This study aims to examine how different hospitals utilize social media to communicate risk information about COVID-19 with the communities they serve, and how hospitals' social media messaging (firm-generated content and their local community's responses (user-generated content) evolved with the COVID-19 outbreak progression.

This research proposes a healthcare-specific social media analytics framework and studied 68,136 tweets posted from November 2019 to November 2020 from a geographically diverse set of ten leading hospitals' social media messaging on COVID-19 and the public responses by using social media analytics techniques and the health belief model (HBM).

The study found correlations between some of the HBM variables and COVID-19 outbreak progression. The findings provide actionable insight for hospitals regarding risk communication, decision making, pandemic awareness and education campaigns and social media messaging strategy during a pandemic and help the public to be more prepared for information seeking in the case of future pandemics.

For hospitals, the results provide valuable insights for risk communication practitioners and inform the way hospitals or health agencies manage crisis communication during the pandemic For patients and local community members, they are recommended to check out local hospital's social media sites for updates and advice.

The study demonstrates the role of social media analytics and health behavior models, such as the HBM, in identifying important and useful data and knowledge for public health risk communication, emergency responses and planning during a pandemic.

The significance of social media in our lives is manifold. The tourism sector closely interacts with existing and potential tourists through social media, and therefore, social media analytics (SMA) play a critical role in the uplift of the sector. Hence, this review focus on the role of SMA in tourism as discussed in different studies over a period of time. The purpose of this paper to present the state of the art on social media analytics in tourism.

The review focuses on identifying different SMA techniques to explore the trends and approaches adopted in the tourism sector. The review is based on 83 papers and discuss the studies related to different social media platforms, the travelers' reactions to a particular place and how the tourism experience is enriched by the way of SMA.

Findings indicate different sentiments associated with tourism and provides a review of tourists’ use of social media for choosing a travel destination. The various analytical approaches, areas such as social network analysis, content analysis, sentiment analysis and trend analysis were found most prevalent. The theoretical and practical implications of SMA are discussed. The paper made an effort to bridge the gap between different studies in the field of tourism and SMA.

SMA facilitate both tourists and tourism companies to understand the trends, sentiments and desires of tourists. The use of SMA offers value to companies for designing quick and adequate services to tourists.

Although knowledge based on business intelligence (BI) is crucial, few studies have explored the core of BI knowledge; this study explores this topic.

The authors collected 1,306 articles and 54,020 references from the Web of Science (WoS) database and performed co-citation analysis to explore the core knowledge of BI; 52 highly cited articles were identified. The authors also performed factor and cluster analyses to organize this core knowledge and compared the results of these analyses.

The factor analysis based on the co-citation matrix revealed seven key factors of the core knowledge of BI: big data analytics, BI benefits and success, organizational capabilities and performance, information technology (IT) acceptance and measurement, information and business analytics, social media text analytics, and the development of BI. The cluster analysis revealed six categories: IT acceptance and measurement, BI success and measurement, organizational capabilities and performance, big data-enabled business value, social media text analytics, and BI system (BIS) and analytics. These results suggest that numerous research topics related to big data are emerging.

The core knowledge of BI revealed in this study can help researchers understand BI, save time, and explore new problems. The study has three limitations that researchers should consider: the time lag of co-citation analysis, the difference between two analytical methods, and the changing nature of research over time. Researchers should consider these limitations in future studies.

This study systematically explores the extent to which scholars of business have researched and understand BI. To the best of the authors’ knowledge, this is one of the first studies to outline the core knowledge of BI and identify emerging opportunities for research in the field.

This study aims to propose an integrated moderated mediation model to examine the process by which hotels obtain competitive advantage from the perspective of hotel managers.

After obtaining the agreement of the participants, a total of 560 candidates, all of whom were hotel managers, completed a survey administered online over a three-month period from November 2020 to January 2021. Ultimately, 257 valid responses were obtained after data screening by research assistants, for a response rate of 45.89%.

By reference to the concepts of big data (BD) and sustainability, the results show that BD management is a foundational attribute with respect to the indirect effects of BD analytics capabilities via proactive market orientation and social media collaboration. Furthermore, sustainability commitments and marketing are found to affect the relationship between such analytics capabilities and competitive advantage. Additionally, information sharing and food services have positive moderating effects that strengthen the changes in critical attributes during the process of obtaining competitive advantage.

To date, despite the proliferation of the concepts of BD, social media and sustainability, there is a lack of adequate empirical evidence and systematic literature reviews to comprehensively synthesize the emerging body of literature in the fields of tourism and hospitality research.

本研究于2020年11月到2021年1月的三个月期间, 在取得560位饭店管理者的同意后, 让他们完成了在线问卷调查。最终, 经过研究助理的数据筛选后, 收集到257份有效问卷, 有效回收率为45.89%。

本研究提出了一个调节式中介模型, 透过酒店管理者的角度来探讨酒店获得竞争优势的过程。

研究结果显示, 大数据管理透过参考大数据和永续的概念, 探讨主动式市场导向和社交媒体协作对大数据分析能力产生间接影响的基本重要归因。此外, 永续承诺和行销会影响分析能力与竞争优势之间的关系。再者, 信息共享和食品服务的关系起着显著的调节作用, 在获得竞争优势的过程中加强了关键属性间的关系。

迄今为止, 尽管大数据、社交媒体和永续的概念逐渐受到重视, 但仍缺乏足够的实证和系统性文献综整来阐逑观光餐旅研究文献缺憾。

Tras obtener el consentimiento de los participantes, un total de 560 candidatos, todos directores de hotel, completaron una encuesta online en un período de tres meses, desde noviembre de 2020 hasta enero de 2021. Finalmente, se obtuvieron 257 respuestas válidas tras un proceso de comprobación por los técnicos de investigación, lo que supone una tasa de respuesta del 45,89%.

Este estudio propone un modelo de mediación moderado integrado para examinar el proceso por el cual los hoteles obtienen ventajas competitivas desde la perspectiva de los directores de hotel.

Haciendo referencia a los conceptos de macrodatos y sostenibilidad, los resultados muestran que la gestión de macrodatos es un atributo fundamental para los efectos indirectos de las capacidades analíticas de macrodatos a través de la orientación proactiva al mercado y la colaboración en los medios sociales. Además, se observa que los compromisos de sostenibilidad y el marketing afectan a la relación entre dichas capacidades analíticas y la ventaja competitiva. Asimismo, el intercambio de información y los servicios de alimentación tienen efectos moderadores positivos que fortalecen los cambios en los atributos críticos durante el proceso de obtención de una ventaja competitiva.

Hasta la fecha, a pesar de la proliferación de los conceptos de macrodatos, medios sociales y sostenibilidad, se carece de evidencia empírica adecuada y de revisiones bibliográficas sistemáticas que sinteticen de forma exhaustiva la literatura emergente en los campos de investigación del turismo y la industria hotelera.

This study evaluates the effect of the media image content of business to business (B2B) organizations to accelerate social media engagement. It highlights the importance of strategically designing image content for business marketing strategies.

This study designed a computation extensive research model based upon the stimulus-organism-response (SOR) theory using 39,139 Facebook posts of 125 organizations selected from Fortune 500 firms. Attributes from images and text were estimated using deep learning models. Subsequently, inferential analysis was established with ordinary least squares regression. Further machine learning algorithms, like support vector regression, k-nearest neighbour, decision tree and random forest, are used to analyze the significance and robustness of the proposed model for predicting engagement metrics.

The results indicate that the social media (SM) image content of B2B firms significantly impacts their social media engagement. The visual and linguistic attributes are extracted from the image using deep learning. The distinctive effect of each feature on social media engagement (SME) is empirically verified in this study.

This research presents practical insights formulated by embedding marketing, advertising, image processing and statistical knowledge of SM analytics. The findings of this study provide evidence for the stimulating effect of image content concerning SME. Based on the theoretical implications of this study, marketing and media content practitioners can enhance the efficacy of SM posts in engaging users.

This study explores the sustainable supply chain trade-offs in the electronics industry.

The study employs a social media analytics approach and analyses Twitter posts from August 2017 to December 2021. Thematic analysis is applied to discover the pattern in sustainable supply chain trade-offs based on the consumers' perceptions. In addition, a chi-square test was used to measure whether a relationship exists between product groups and sustainable supply chain perceptions.

The results indicate that environmental practices are the most frequent topic among consumers on social media. Further, although basic sustainable supply chain practices are prioritised in the environmental aspect, advanced sustainable supply chain practices take precedence over basic ones in the social dimension. The result from the chi-square independence test reveals that there is no significant relationship between different products and perceptions of consumers except for economically advanced sustainable supply chain practices.

The main implications of the present study are to offer a fast and efficient method to marketers and companies for discovering customer perceptions. In a way, they can identify where the quality of practices needs to improve in their supply chains to gain customer satisfaction. Additionally, the authors suggest industries declare their trade-off preferences between sustainable supply chain practices transparently.

The findings extend the abundance of sustainable supply chain literature by identifying the sustainable supply chain trade-offs among consumer electronics. Also, the reason for customers' dissatisfaction is provided. In the end, six propositions are presented based on the explorations.

This study aims to explore the emergence of the human resource (HR) analyst role. The job posts on LinkedIn display the industry demand and skills required by the organizations. This study identifies the different knowledge, skills and abilities (KSA) required for an HR analyst role in different stages of professional growth (i.e. entry-level, middle-senior level and top-level) across different industries/sectors as applicable to the crisis.

A total of 80 job posts were extracted from LinkedIn. Details such as industry, job levels, qualifications, job experience, job functions, job descriptions (JDs) and job skills (JS) were collected. Further, 30 videos were extracted from YouTube and converted into text. Text analysis was conducted using NVivo software to analyze JDs, JS and job functions. Using NVivo, word frequency, word cloud, word tree and treemap were created to visualize the data. Finally, ten in-depth interviews were conducted with senior HRA managers based in India to understand the essential competencies required for the HR analyst role and the strategies to develop them.

The findings indicate that not only technical skills are needed, but business and communication skills are particularly important for all job levels during a crisis. The JD word cloud showed words, such as data, business, support and management, and the word tree depicted HR data and change agents as important words with many related sentences as branches. General JS included analytical, communication, problem-solving and management. Technical JS were the most widely used and included structure query language, system applications & products in data processing, human capital management, TABLEAU, management information system and PYTHON. Strategies to develop these competencies included case studies, live projects, internships on HR analytics (HRAs) assignments and mentoring by senior HRA professionals.

The sample used was small, as the study included 80 job posts available on LinkedIn restricted to India. The study was restricted to qualitative approach and text analytics was used. Survey methods and a quantitative approach can be used to collect data from HR recruiters, job holders and senior leaders to understand the role of HRAs in the job market and then these variables can be tested empirically.

Based on the McCartney et al.’s (2020) competency model for the HR Analyst role, this study has explored the KSA framework using data visualization techniques and used text analytics to analyze LinkedIn job posts for different levels, videos from YouTube and in-depth interviews. It also mapped the KSA for the HR analyst role to the various stages of crisis system management given by Mitroff (2005). The use of social media analytics, such as analyzing LinkedIn data and YouTube videos, are highlighted.