Social media data analytics for business decision making system to competitive analysis

The circular economy through the prism of machine learning and the YouTube video media platform

The transition to a Circular Economy (CE) is rapidly gaining ground across countries and industries. It is the means of achieving more sustainable development by adopting innovative environmentally friendly strategies and saving primary resources. There are several studies indicating the increasing public and corporate interest in the CE but still remain limited in terms of the multitude and utilization of social media data. This work aims to shed light on the most common topics discussed on the YouTube platform, related to the CE. For this reason, we selected 17 videos including the term "Circular Economy" since these have been the most relevant with a sufficient number of comments and views. The model identified two main topics referring to "Sustainable industry and environmental responsibility" and "Circular Economy and resource management" which is a strong indicator of the people's interest in the utilization of resources alongside industrial and corporate activities. The two-topic configuration presented the highest coherence score; however, five and ten-topic configurations have been deployed since there was no extreme differentiation in the model's performance, which could provide more detailed insights. This work's innovation lies in utilizing Machine Learning techniques and social media data to unravel CE's debates.

A Geospatial Drug Abuse Risk Assessment and Monitoring Dashboard Tailored for School Students: Development Study With Requirement Analysis and Acceptance Evaluation

BACKGROUND

The enormous consequences of drugs include suicides, traffic accidents, and violence, affecting the individual, family, society, and country. Therefore, it is necessary to constantly identify and monitor the drug abuse rate among school-going youth. A geospatial dashboard is vital for the monitoring of drug abuse and related crime incidence in a decision support system.

OBJECTIVE

This paper mainly focuses on developing MyAsriGeo, a geospatial drug abuse risk assessment and monitoring dashboard tailored for school students. It introduces innovative functionality, seamlessly orchestrating the assessment of drug abuse usage patterns and risks using multivariate student data.

METHODS

A geospatial drug abuse dashboard for monitoring and analysis was designed and developed in this study based on agile methodology and prototyping. Using focus group and interviews, we first examined and gathered the requirements, feedback, and user approval of the MyAsriGeo dashboard. Experts and stakeholders such as the National Anti-Drugs Agency, police, the Federal Department of Town and Country Planning, school instructors, students, and researchers were among those who responded. A total of 20 specialists were involved in the requirement analysis and acceptance evaluation of the pilot and final version of the dashboard. The evaluation sought to identify various user acceptance aspects, such as ease of use and usefulness, for both the pilot and final versions, and 2 additional factors based on the Post-Study System Usability Questionnaire and Task-Technology Fit models were enlisted to assess the interface quality and dashboard sufficiency for the final version.

RESULTS

The MyAsriGeo geospatial dashboard was designed to meet the needs of all user types, as identified through a requirement gathering process. It includes several key functions, such as a geospatial map that shows the locations of high-risk areas for drug abuse, data on drug abuse among students, tools for assessing the risk of drug abuse in different areas, demographic information, and a self-problem test. It also includes the Alcohol, Smoking, and Substance Involvement Screening Test and its risk assessment to help users understand and interpret the results of student risk. The initial prototype and final version of the dashboard were evaluated by 20 experts, which revealed a significant improvement in the ease of use (P=.047) and usefulness (P=.02) factors and showed a high acceptance mean scores for ease of use (4.2), usefulness (4.46), interface quality (4.29), and sufficiency (4.13).

CONCLUSIONS

The MyAsriGeo geospatial dashboard is useful for monitoring and analyzing drug abuse among school-going youth in Malaysia. It was developed based on the needs of various stakeholders and includes a range of functions. The dashboard was evaluated by a group of experts. Overall, the MyAsriGeo geospatial dashboard is a valuable resource for helping stakeholders understand and respond to the issue of drug abuse among youth.

Enhancing decision-making support by mining social media data with social network analysis

This paper explores the use of social network analysis (SNA) on airlines' online social networks (OSNs) to extract valuable information for decision support, by analyzing interactions and discursive exchanges between users. The research is focused on fostering customer service of an airline company during a strike period, namely by detecting influential customers (whether satisfied or dissatisfied), address pending requests, and enhancing customer satisfaction, thus promoting issue-solving, and increasing responsiveness. The methodology involves analyzing data from the Facebook account of an airline company, using SNA to structure the data, and calculating metrics to detect possible situations to be addressed by customer service. The research concludes that it is possible to extract valuable information for decision support by analyzing the metrics that were built over the interactions and discursive exchanges between OSN users. SNA metrics enable to measure airline's call-center performance in terms of speed of answer and customer satisfaction, to identify active users requiring additional support, as well as highly influential customers who may impact on the overall customer satisfaction, thus helping to resolve issues more efficiently. This study provides both theoretical and practical implications: it contributes to the existing literature by integrating social interaction and SNA for decision support in airline's service context; and it provides practical insights into how companies can use SNA metrics to improve customer service. The research also highlights and corroborates the importance of monitoring social media interactions for decision-making and improving customer service.

Towards Data-Driven Decision-Making in the Korean Film Industry: An XAI Model for Box Office Analysis Using Dimension Reduction, Clustering, and Classification

The Korean film market has been rapidly growing, and the importance of explainable artificial intelligence (XAI) in the film industry is also increasing. In this highly competitive market, where producing a movie incurs substantial costs, it is crucial for film industry professionals to make informed decisions. To assist these professionals, we propose DRECE (short for Dimension REduction, Clustering, and classification for Explainable artificial intelligence), an XAI-powered box office classification and trend analysis model that provides valuable insights and data-driven decision-making opportunities for the Korean film industry. The DRECE framework starts with transforming multi-dimensional data into two dimensions through dimensionality reduction techniques, grouping similar data points through K-means clustering, and classifying movie clusters through machine-learning models. The XAI techniques used in the model make the decision-making process transparent, providing valuable insights for film industry professionals to improve the box office performance and maximize profits. With DRECE, the Korean film market can be understood in new and exciting ways, and decision-makers can make informed decisions to achieve success.

Implementation model of data analytics as a tool for improving internal audit processes

Introduction

The aim of this article is to understand the importance of internal audit departments todays-part of corporate governance and guardian of the organisation's culture and climate-, as well as the opportunities that new technologies offer to increase their effectiveness and efficiency.

Methods

To this end, based on an exhaustive review of the literature, the concepts of internal audit and data analytics are related, and a framework is proposed for the implementation of a technology of these characteristics in an internal audit department.

Results

The results of the research show that those companies that invest resources in readapting their processes to technological change are likely to obtain better results than those organisations that keep their management procedures obsolete.

Discussion

Based on these results, it is concluded that there is a need to consider technological change in internal audit departments, specifically data analytics, to increase the effectiveness and efficiency of audit processes.

Artificial Intelligence and Sentiment Analysis: A Review in Competitive Research

As part of a business strategy, effective competitive research helps businesses outperform their competitors and attract loyal consumers. To perform competitive research, sentiment analysis may be used to assess interest in certain themes, uncover market conditions, and study competitors. Artificial intelligence (AI) has improved the performance of multiple areas, particularly sentiment analysis. Using AI, sentiment analysis is the process of recognizing emotions expressed in text. AI comprehends the tone of a statement, as opposed to merely recognizing whether particular words within a group of text have a negative or positive connotation. This article reviews papers (2012–2022) that discuss how competitive market research identifies and compares major market measurements that help distinguish the services and goods of the competitors. AI-powered sentiment analysis can be used to learn what the competitors’ customers think of them across all aspects of the businesses.

Linking knowledge acquisition, knowledge dissemination, and manufacturing SMEs’ sustainable performance: the mediating role of knowledge application

Purpose

This study aims to examine the impact of knowledge acquisition and dissemination on the sustainable performance of small- and medium-sized enterprises (SMEs). Additionally, the mediating role of knowledge application in this relationship is investigated.

Design/methodology/approach

Data collected from 428 manufacturing SMEs in Malaysia were analyzed using the partial least squares structural equation modeling approach.

Findings

The results reveal that knowledge acquisition significantly impacts the sustainable performance of manufacturing SMEs, and knowledge application partially mediates this impact. Cutting-edge knowledge from within or outside the organization enables a deeper understanding of information regarding new products/services, a list of competing suppliers, consumer preferences and so on.

Research limitations/implications

This study combined the knowledge-based view theory and the triple bottom line framework into a unified conceptual framework and examined the impact of knowledge acquisition and dissemination on manufacturing SMEs’ sustainable performance as well as the mediating role of knowledge application on manufacturing SMEs’ sustainable performance.

Practical implications

Manufacturing SMEs should emphasize knowledge application in business operations and encourage employees to develop and enhance their knowledge acquisition and dissemination to leverage knowledge in ways that enhance business operations, produce new products and generate new knowledge assets.

Originality/value

This study is unique in that it highlights the partially mediated effects of knowledge application on the linkages between knowledge acquisition and manufacturing SMEs’ sustainable performance and the fully mediated effects of knowledge application on the association between knowledge dissemination and sustainable performance of manufacturing SMEs.

Social media analytical CRM: a case study in a bank

The use of the social media (SM) has become more and more widespread during the last two decades, the companies started looking for insights for how they can improve their businesses using the information accumulating therein. In this regard, it is possible to distinguish between two lines of research: those based on anonymous data and those based on customer specific data. Although obtaining customer specific SM data is a challenging task, analysis of such individual data can result in very useful insights. In this study we take up this path for the customers of a bank, analyze their tweets and develop three kinds of analytical models: clustering, sentiment analysis and product propensity. For the latter one, we also develop a version where, besides the text information, the structural information available in the bank databases are also used in the models. The result of the study is a considerably more efficient set of analytical CRM models.

Methodology for Assessing the Digital Image of an Enterprise with Its Industry Specifics

This study provides a framework for the comparative assessment of the key industry aspects of competitiveness among logistics services and the logistics systems of enterprises in the informational environment. Frequently, the relationships between a consumer and a company created by means of the informational environment determine how the enterprise positions itself in the market. For instance, the evaluation of a company’s representation in the information field is an essential aspect of determining the company’s competitiveness. The study suggests a set of special metrics for measuring the representation of digital components and other aspects of an enterprise’s digital image via data gathering and analysis of the most encountered tokens. The proposed automated analysis algorithm allows companies to examine their image in the digital environment and implement effective decisions. The functionality of the algorithm fosters data collection, helping to form the desired image of the company. Tokens of several thematic groups on social media are collected during the process, and the most significant of them that are valuable for the competitiveness of the enterprise are extracted. The outcome can be used for the tracking of the dynamics of key parameters of an enterprise’s image and for conducting a comparative analysis of the digital image of its competitors.

Role of Knowledge Management on the Sustainable Environment: Assessing the Moderating Effect of Innovative Culture

Environmental sustainability has become the need of the hour and has been emphasized immensely because of the increased environmental awareness and resulting problems caused due to negligence. This study has intended to determine the role of knowledge management (KM) practices in achieving a sustainable environment with the mediating role of environmental awareness and green technological use. The study further examined the moderating role of green innovative culture between the relationship of KM practices and a sustainable environment. The data were acquired from 378 managerial level personnel of the construction industry in China through questionnaires. Smart-PLS 3.3.3 was used to determine the study's hypothesis through the structural equation modeling (SEM) technique. The study found that KM practice has a significant relationship with a sustainable environment, environmental awareness, and green technological use. Also, environmental awareness has a significant effect on a sustainable environment. Moreover, it was found in the study that environmental awareness significantly mediated the relationship between KM practices and sustainable environment, but green technological use did not find any mediating effect on the relationship between KM practices and sustainable environment. Furthermore, green innovative culture considerably moderated the relationship between KM practices and a sustainable environment. Theoretically, this study contributes to the existing literature by incorporating and investigating the role of KM practices in a sustainable environment. Practically, this article presented some implications for the management concerning promoting KM practices and environmental awareness within the organization and developing a green innovative culture.