International Journal of Web Based Communities (10 papers in press)

Regular Issues

• The Digital Presence of Law Firms: A Study of Social Media Strategies Employed by Prestigious US Law Firms  
  by Sara García-Moreno, Maria Elena Aramendia-Muneta 
  Abstract: The increasing digitalisation has brought to the fore the importance of building and consolidating the digital presence of law firms on the internet. Through the analysis of different accounts on various social networks (Twitter, LinkedIn, Facebook, Instagram, YouTube, Podcast), we will study the strategy of some of the most prestigious law firms in the USA (DLA Piper, Baker McKenzie, Norton Rose Fulbright, Latham & Watkins and White & Case). This paper aims to deepen existing research on social media strategies, as well as to encourage future analysis of law firms in this area. A general tendency has been observed to show the human side of such firms as well as the different pro bono services they are engaged in. The main conclusions found that Law firms need to develop different strategies for each social network and accurately define the target audience in each network.
  Keywords: social media; law firms; marketing strategy; content; Twitter; LinkedIn; Facebook; Instagram.
  DOI: 10.1504/IJWBC.2024.10064760
   
  
• Does Technology Readiness Influence Sustained Use of Social Media Platform? A Cross-National Comparison  
  by Xinsheng Zhao, SungJoon Yoon 
  Abstract: This study attempts to validate an extended technology readiness and technology acceptance model (TRAM), which embraces an emotional feedback measure (enjoyment) to predict the continuous use of social media platforms. In addition, this study aims to determine the differences in the factors affecting sustained use of social media platform between Chinese and Korean users. The result indicated positive technology readiness having a positive impact on five factors (performance expectancy, effort expectancy, asocial influence, facilitating conditions and enjoyment) that were hypothesised to motivate technology acceptance. In addition, the study found that three technology acceptance motivators (performance expectancy, effort expectancy, and enjoyment significantly affect continuous use intention. Finally, the study found that positive technology readiness exerts greater significant effect on technology acceptance factors for China than Korea.
  Keywords: technology acceptance; social media platform; technology readiness; reuse intention; enjoyment; cross-national difference.
  DOI: 10.1504/IJWBC.2024.10064761
   
  
• The Boardroom's Digital Footprint: Exploring the Impact of Diversity on Web-Based Disclosures  
  by Martin Surya Mulyadi, Yunita Anwar 
  Abstract: This study investigates the influence of board diversity on the extent of web-based corporate disclosures within the top publicly listed corporations in Indonesia and Malaysia. Grounded in media agenda-setting theory and using multiple regression analysis, it reveals a significant but nuanced impact of board diversity on disclosure practices. The study finds that while board nationality diversity negatively impacts web-based disclosures, robust corporate governance can mitigate this effect. Conversely, the diversity of foreign educational backgrounds and board gender diversity does not significantly influence web-based disclosures. These findings underscore the importance of a nuanced understanding of board diversity and its interaction with corporate governance in shaping disclosure practices. The research contributes to corporate web disclosure theory and offers practical insights for corporations seeking to optimise their web-based disclosures.
  Keywords: board diversity; web-based corporate disclosures; media agenda setting theory.
  DOI: 10.1504/IJWBC.2024.10064762
   
  
• Polarization, Filter Bubbles and Radicalization on YouTube: a Systematic Literature Review  
  by Gustavo Almeida, Ana C. B. Garcia, Jefferson Elbert Simões 
  Abstract: Polarization and online radicalization pose significant threats in the modern world. YouTube, particularly its recommendation system
  Keywords: YouTube; echo chambers; filter bubbles; polarization; radicalization.
  DOI: 10.1504/IJWBC.2025.10069882
   
  
• Influencing Factors and Driving Paths of User Stickiness Under Live-Streaming Assistance to Agriculture: A Configuration Analysis Utilizing the TOE Framework  
  by Lin Wang, Chen CUI, Jiangli Zhang, Yang Zhao 
  Abstract: The spread of internet technology has sparked considerable attention towards live-streaming, which holds the capacity to markedly boost agricultural product sales and, consequently, invigorate the development of rural economies. User stickiness, denoting the extent of consumer engagement with live-streaming platforms, is a crucial element in driving the inclination to purchase. The stimuli present within live-streaming environments are known to shape user stickiness; however, the specific dynamics governing this influence remain to be fully elucidated. Based on the Technology-Organization-Environment framework, explore cues from different actors: live-streaming platform, hosts, and consumers, to construct a configurational effect model of technological affordance, emotional labor, quasi-social interaction, flow experience, and the formation of user stickiness. We analyze a sample of 350 questionnaires using fsQCA. It is determined that user stickiness arises from the interplay of various elements, with the interactive conduct between hosts and audience members exerting a more significant influence on user stickiness.
  Keywords: User stickiness; Live streaming of agricultural products; Emotional labor; Flow experience; Quasi-social interaction; Affordance; fsQCA.
  DOI: 10.1504/IJWBC.2025.10070314
   
  

Special Issue on: Consumer Behaviour in Mobile Commerce and Social Media-Part 2

• Study on Detection of Impulsive Purchase Behavior of E-commerce Platform Consumers Based on Social Network Media  
  by Bo An 
  Abstract: Studying consumers’ impulsive purchasing behaviour helps to understand their purchasing behaviour and increase sales revenue. Therefore, this article proposes a method for detecting consumer impulse buying behaviour on e-commerce platforms based on social network media. Firstly, collect data on consumer purchasing behaviour; Secondly, preprocess the characteristics of impulse buying behaviour based on the RFM function. Then, considering the polarity and degree of emotional words, calculate impulsive emotion scores based on an emotion dictionary; Finally, use the LSH algorithm to find the nearest neighbour point that matches each user’s emotional needs, and use the input of LOF to find the extreme point, obtaining the detection results of impulse buying behaviour. The results show that the detection recall rate of this method can reach 99.0%, the detection error is only 0.02, and the detection time is only 8.9 seconds. The detection effect of this method is good.
  Keywords: social network media; Impulsive purchasing behaviour; e-commerce platforms; K-nearest neighbour method; LOF method; LSH algorithm.
  DOI: 10.1504/IJWBC.2024.10061785
   
  
• Study on Distributed network anomaly attack detection method based on machine learning  
  by Qiaoyun Chen, Youyou Li 
  Abstract: To overcome the problems of traditional methods such as low detection accuracy, high false alarm rate and long detection time, a distributed network anomaly attack detection method based on machine learning is proposed. Firstly, the local density of network operation data points is estimated by combining the Gaussian kernel and cut-off check, and the network operation data is clustered by the DPCA algorithm. Secondly, through the constructed attack model, abnormal attack characteristics are determined and important features are screened. Finally, the naive Bayes in machine learning is used to determine the attribute characteristics of each category in the clustering results. Match the category attribute feature with the important feature to get the anomaly attack detection result. The experimental results show that the maximum detection accuracy of this method is 98%, the average false alarm rate is 2.64%, and the detection time varies between 0.25 s and 0.68 s.
  Keywords: machine learning; distributed; network abnormality; attack detection; DPCA algorithm; naive Bayes.
  DOI: 10.1504/IJWBC.2024.10061786
   
  
• A Precise sensing method of campus network security situation based on fuzzy clustering algorithm  
  by Ranran Yin, Zhenyu Yang 
  Abstract: To ensure the safe operation of the campus network and improve the sensing accuracy and convergence speed, a precise sensing method for campus network security situations based on a fuzzy clustering algorithm is proposed. Firstly, the constructed element model is used to extract the situation elements, and the situation information is processed through the non-negative matrix decomposition algorithm. Secondly, the Kalman entropy method is used to estimate the security situation of the whole network of the network campus, and the new information on the network security situation is calculated. Finally, according to the characteristics of campus network security situation awareness, the network security situation awareness is realised through a fuzzy clustering algorithm. The experimental results show that the MAPE value and RMSE value of the proposed method are low, and the RMSE value is maintained below 0.15, the convergence speed is fast, and can comprehensively reflect the network security situation.
  Keywords: fuzzy clustering algorithm; campus network; security situation awareness; Kalman filter model; transitive closure.
  DOI: 10.1504/IJWBC.2024.10061787
   
  
• Abnormal Behavior Detection of E-commerce Consumers Based on Improved Hidden Markov Model  
  by Meng Su  
  Abstract: To address the issues of low anomaly detection rate, high false positive rate, and long detection time in traditional methods, an abnormal behaviour detection method for e-commerce consumers based on an improved hidden Markov model is proposed. The Scrapy spider framework is used to collect e-commerce consumer behaviour data, including purchase data, browsing data, search data, and evaluation data. The collected data is processed using an improved K-means algorithm for clustering, with normalisation, missing value imputation, and outlier removal applied to the clustering results. The MOPSO algorithm is used to optimise the parameters of the hidden Markov model, and the processed data is then input into the improved hidden Markov model to output the relevant detection results. Experimental results show that the maximum anomaly detection rate of this method is 96.7%, the maximum false positive rate is 4.7%, and the average detection time is 0.73 s.
  Keywords: improved hidden Markov model; e-commerce consumers; abnormal behaviour detection; Scrapy crawler architecture; improved k-means algorithm; MOPSO algorithm.
  DOI: 10.1504/IJWBC.2024.10062542
   
  
• The Moderating Role of Product Involvement in Virtual Reality-Enhanced Online Immersion: Effects on Internet Consumer Behaviour  
  by Imène Ben Yahia, Salma Ayari 
  Abstract: Despite its importance, the mental imagery process has often been overlooked in research on internet user behaviour. This study explores the effects of immersion in a merchant website enhanced by virtual reality devices on mental imagery, as well as on the intention to buy and revisit. A quantitative study involving 350 internet users highlighted the impact of the immersive online experience on mental imagery and internet user responses. The study also examines the moderating effect of product involvement. The results of an experiment validate the hypotheses and provide valuable managerial recommendations. This research contributes significantly to both academics and professionals.
  Keywords: commercial website; online immersion; mental imagery; virtual reality; product involvement.
  DOI: 10.1504/IJWBC.2025.10066227