Social media analytics and business intelligence research: A systematic review

Diachronic profile of startup companies through social media

Social media platforms have become powerful tools for startups, helping them find customers and raise funding. In this study, we applied a social media intelligence-based methodology to analyze startups’ content and to understand how their communication strategies may differ during their scaling process. To understand if a startup’s social media content reflects its current business maturation position, we first defined an adequate life cycle model for startups based on funding rounds and product maturity. Using Twitter as the source of information and selecting a sample of known Portuguese IT startups at different phases of their life cycle, we analyzed their Twitter data. After preprocessing the data, using latent Dirichlet allocation, topic modeling techniques enabled the categorization of the data according to the topics arising in the published contents of the startups, making it possible to discover that contents can be grouped into five specific topics: “Fintech and ML,” “IT,” “Business Operations,” “Product/Service R&D,” and “Bank and Funding.” By comparing those profiles against the startup’s life cycle, we were able to understand how contents change over time. This provided a diachronic profile for each company, showing that while certain topics remain prevalent in the startup’s scaling, others depend on a particular phase of the startup’s cycle. Our analysis revealed that startups’ social media content differs along their life cycle, highlighting the importance of understanding how startups use social media at different stages of their development.

Implementation of a conversational virtual assistant for open government data portal: Effects on citizens

Insufficient support services are one of the barriers for citizen’s open government data (OGD) utilisation. Considering the strengths and effectiveness of virtual assistants (VAs) in providing instant and user-friendly support services, this study focuses primarily on its implementation for OGD portals. A conversational VA prototype is specifically developed. A between-subjects experiment with one factor (VA/text-based support services) was carried out. Results show citizens’ positive attitudes towards VAs providing support services on OGD portals. No significant difference was found for citizens’ acceptance, trustworthiness and rapport for OGD portals with and without a VA. Accuracy rate of completing tasks with VAs is slightly higher. A correlation lies between trustworthiness and rapport with OGD portals. This study benefits the improvement of OGD portals’ online services to enhance citizens’ experiences and provides a research model both for investigating VAs’ effects and for comparing different designs of a same kind of service for an interface.

An empirical study of entrepreneurial leadership and fear of COVID-19 impact on psychological wellbeing: A mediating effect of job insecurity

The empirical study proposes a model for investigating the effect of entrepreneurial leadership on job insecurity and employee psychological wellbeing during COVID-19 based on the combined theoretical grounds of The Conservation of Resources Theory and Social Learning. To explore the job insecurity relationship with psychological wellbeing, and measure the impact of Fear of COVID-19, an empirical study was conducted on a sample of 408 employees in Croatia. The data of the cross-sectional study was collected in November and December 2020. A strong influence of job insecurity on the psychological wellbeing of employees has been identified. Furthermore, fear of COVID-19 was found to have adverse psychological effects on wellbeing. The theorized positive impact of entrepreneurial leadership on job insecurity was not supported by the evidence. The strong point of our contribution lies in the finding that the entrepreneurial leadership style alone does not buffer against job insecurity, thus pointing that the more comprehensive inquiry into other organizational factors, such as coping, learning abilities, developmental opportunities, personal disposition, and pressure bearing. The research is the first step toward enhancing our understanding of the entrepreneurial dimension of transactional psychology. The observations we recorded have implications for research into the study of the mental processes and their impact on organizational proactive behavior.

A Technology Watch/Competitive Intelligence–based Decision-Support System optimised with Genetic Algorithms

To survive and prosper in a highly competitive environment where uncertainty and ambiguity are the norm, today’s firms are faced with the need for new information management methods and tools. Two of the most prominent strategies that take information and its treatment as a value-generating element in firms’ decision-making are Technology Watch and Competitive Intelligence. In addition, one of the fundamental components that a system based on these strategies must have is an efficient method of Information Retrieval. The present study describes a Competitive Intelligence–based decision-support system that uses a Genetic Algorithm. The system contributes to improving information retrieval through search optimisation, thus enhancing the performance of this knowledge-generating tool for organisations.

Identification of patent-based inventor competencies: An approach for partially automated competence retrieval in technological fields

BACKGROUND: Semantic analyses of patents have been used for years to unlock technical knowledge. Nevertheless, information retrievable from patents remains widely unconsidered when making strategic decisions, when recruiting candidates or deciding which qualifications to offer to employees in technological fields. OBJECTIVES: This paper provides an approach to evaluate whether competencies and competence demands in technological fields can be derived from patents and if this process can be automated to a certain extent. METHODS: A sample of significant patents is analyzed with regard to comprised competence data via semantic structures like n-gram and Subject-–Action–Object (SAO) analysis. The retrieved data is cleansed and matched semantically to inventor competencies from social career networks and checked for similarities. RESULTS: A social career network profile analysis of significant inventors revealed a total of 570 competencies that were matched with the results of the n-gram and SAO analysis. Overall, 15%of the extracted social career network competence data were covered through extracted n-grams (87 out of 570 terms), while the SAO analysis showed a match rate of 18.8%, covering 107 terms. CONCLUSIONS: The outlined approach suggests a partly automatable process of promising character to identify technological competence demands in patents.

Future competencies in human-machine interaction: An interdisciplinary approach for the anticipation of strategically relevant competencies in the field of automotive human-machine interaction

BACKGROUND

Digitalization and technological progress lead to an increasingly fast development of promising fields for action and new technologies whereas the time required to qualify employees for new activities and work content has remained largely the same. Organizations have to establish anticipative competence measures to secure their competitiveness.

OBJECTIVES

Those developments suggest that a new approach to develop human resource development strategies is required.

METHODS

This article describes the results of a competence survey that was developed in an interdisciplinary approach between organizational psychology and futurology and conducted in the field of automotive Human-Machine Interaction (HMI) research. The content of the questionnaire is based on a series of expert interviews focusing and a data-driven approach that scanned significant patents for competence demand data.

RESULTS

The conducted ANOVAs show that both sources for data retrieval create relevant items even though experts from the conceptual field rate data-based items significantly less relevant than the other participants. Moreover, interview-based items lead to significantly more relevant ratings in methodological fields while data-driven items were rated significantly more relevant for the technological area.

CONCLUSIONS

Even though there are some uncertainties to examine, the displayed approach seems promising for the derivation of more detailed and enriched future competency demands in technological fields.

Social Media Big Data Analysis: Towards Enhancing Competitiveness of Firms in a Post-Pandemic World

In this paper, we proposed an advanced business intelligence framework for firms in a post-pandemic phase to increase their performance and productivity. The proposed framework utilizes some of the most significant tools in this era, such as social media and big data analysis for business intelligence systems. In addition, we survey the most outstanding related papers to this study. Open challenges based on this framework are described as well, and a proposed methodology to minimize these challenges is given. Finally, the conclusion and further research points that are worth studying are discussed.

Understanding the Dark Side of Online Reviews on Consumers' Purchase Intentions in E-Commerce: Evidence From a Consumer Experiment in China

Background: Online review, as an important way of electronic word-of-mouth (eWOM) communication, plays an important role in e-commerce. However, few studies have examined the dark side of online reviews and their effect on consumers' purchase intentions. Information inconsistency is one of the dark sides that plays a critical role in influencing consumers' purchase intentions through online reviews.

Methods: Using a 2^*2 between-subject design that explores the main effects of the type of information inconsistency (vertical- vs. horizontal-attribute inconsistency) on purchase intention and the moderating effect of product type (search vs. experience product).

Results: This study examines whether and how the type of information inconsistency between online recommendations and reviews influences consumer purchase decision-making.

Conclusions: The findings show that vertical-attribute inconsistency leads to a lower purchase intention for search products; moreover, both vertical- and horizontal-attribute inconsistencies have no significant effect on purchase intention for experience products.

Exploring and Predicting the Knowledge Development in the Field of Energy Storage: Evidence from the Emerging Startup Landscape

The distribution and deployment of energy storage systems on a larger scale will be a key element of successfully managing the sustainable energy transition by balancing the power generation capability and load demand. In this context, it is crucial for researchers and policy makers to understand the underlying knowledge structure and key interaction dynamics that could shape the future innovation trajectory. A data-driven approach is used to analyze the evolving characteristics of knowledge dynamics from static, dynamic and future-oriented perspective. To this end, a network analysis was performed to determine the influence of individual knowledge areas. Subsequently, an interaction trend analysis based on emergence indicators was conducted to highlight the promising relations. Finally, the formation of new knowledge interactions is predicted using a link prediction technique. The findings show that ensuring the energy efficiency is a key issue that has persisted over time. In future, knowledge areas related to digital technologies are expected to gain relevance and lead the transformative change. The derived insights can assist R&D managers and policy makers to design more targeted and informed strategic initiatives to foster the adoption of energy storage solutions.

Exploring Sources of Satisfaction and Dissatisfaction in Airbnb Accommodation Using Unsupervised and Supervised Topic Modeling

This study aims to examine key attributes affecting Airbnb users' satisfaction and dissatisfaction through the analysis of online reviews. A corpus that comprises 59,766 Airbnb reviews form 27,980 listings located in 12 different cities is analyzed by using both Latent Dirichlet Allocation (LDA) and supervised LDA (sLDA) approach. Unlike previous LDA based Airbnb studies, this study examines positive and negative Airbnb reviews separately, and results reveal the heterogeneity of satisfaction and dissatisfaction attributes in Airbnb accommodation. In particular, the emergence of the topic “guest conflicts” in this study leads to a new direction in future sharing economy accommodation research, which is to study the interactions of different guests in a highly shared environment. The results of topic distribution analysis show that in different types of Airbnb properties, Airbnb users attach different importance to the same service attributes. The topic correlation analysis reveals that home like experience and help from the host are associated with Airbnb users' revisit intention. We determine attributes that have the strongest predictive power to Airbnb users' satisfaction and dissatisfaction through the sLDA analysis, which provides valuable managerial insights into priority setting when developing strategies to increase Airbnb users' satisfaction. Methodologically, this study contributes by illustrating how to employ novel approaches to transform social media data into useful knowledge about customer satisfaction, and the findings can provide valuable managerial implications for Airbnb practitioners.