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The twenty‐first century knowledge driven economy has seen increasing importance being placed on maximising the organisation's intellectual capital (IC). At the same time knowledge management (KM) systems are being developed. The paper establishes similarities between the two and proceeds to develop a systematic approach to linking them through the intellectual capital web (ICW). There are six components with the ICW: strategic objectives, management systems, measurement systems, knowledge workers, catalysts and reward and incentive systems. The integration of IC and KM requires alignment of KM processes with IC assets to meet the organisation's strategic needs. A theoretical conjecture is developed in which the components of elements of ICW are interweaved to achieve strategic objectives. The systematic approach outlined in the paper should offer organisations valuable guidelines to maximising their IC assets and managing their knowledge management processes.

Online social networks are increasingly important venues for businesses to promote their products and image. However, information propagation in online social networks is significantly more complicated compared to traditional transmission media such as newspaper, radio, and television. In this chapter, we will discuss research on modeling and forecasting diffusion of virally marketed content in social networks. Important aspects include the content and its presentation, the network topology, and transmission dynamics. Theoretical models, algorithms, and case studies of viral marketing will be explored.

The factors that motivate Generation Z individuals to use the Internet of Things for security purposes have yet to be explored. Therefore, the purpose of this paper is to close a research gap by verifying the protection motivation theory using gender as a moderator.

The authors used a purposive sampling approach to collect data from Dhaka city, in which 370 valid responses were selected. Additionally, the quantitative and cross-sectional survey used a seven-point Likert scale. Afterward, the evaluation approach included three phases: a measurement model, a structural model and multi-group analysis.

Vulnerability, self-efficacy and response-efficacy were discovered to be critical predictors with a variance of 60.4%. Moreover, there was a significant disparity between males and females in two relationships, response efficacy and intention as well as response cost and intention.

This research expands our understanding of Generation Z consumers' behavioral intentions to take measures against household threats, allowing preventative programs to be improved. Further, in the case of applying coping strategies, a practical difference between males and females has been found that must be bridged through awareness campaigns.

This study has made a unique contribution to the information system literature. First, the role of protection motivation theory factors in addressing security concerns in homes has been assessed. Second, the coping evaluation process has a greater impact on users' intentions than the threat appraisal process. However, males and females use slightly different approaches to defending themselves against the threat.

This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies have largely implemented unweighted (binary) network analysis, or have constructed a weighted network, limited by unweighted centrality measures. This study applies weighted centrality measures (mean association [MA], triangle betweenness centrality [TBC], and weighted harmonic centrality [WHC]) to represent traffic dynamics in HSR and air transportation weighted networks, where nodes represent cities and links represent passenger traffic. The spatial distribution of centrality results is visualized by using ArcGIS 10.2. Moreover, we analyze the network robustness of HSR, air transportation, and multimodal networks by measuring weighted efficiency (WE) subjected to the highest weighted centrality node attacks. In the HSR network, centrality results show that cities with a higher MA are concentrated in the Yangtze River Delta and the Pearl River Delta; cities with a higher TBC are mostly provincial capitals or regional centers; and cities with a higher WHC are grouped in eastern and central regions. Furthermore, spatial differentiation of centrality results is found between HSR and air transportation networks. There is a little bit of difference in eastern cities; cities in the central region have complementary roles in HSR and air transportation networks, but air transport is still dominant in western cities. The robustness analysis results show that the multimodal network, which includes both airports and high-speed rail stations, has the best connectivity and shows robustness.

This article is motivated by the lack of flexibility in Bayesian quantile regression for ordinal models where the error follows an asymmetric Laplace (AL) distribution. The inflexibility arises because the skewness of the distribution is completely specified when a quantile is chosen. To overcome this shortcoming, we derive the cumulative distribution function (and the moment-generating function) of the generalized asymmetric Laplace (GAL) distribution – a generalization of AL distribution that separates the skewness from the quantile parameter – and construct a working likelihood for the ordinal quantile model. The resulting framework is termed flexible Bayesian quantile regression for ordinal (FBQROR) models. However, its estimation is not straightforward. We address estimation issues and propose an efficient Markov chain Monte Carlo (MCMC) procedure based on Gibbs sampling and joint Metropolis–Hastings algorithm. The advantages of the proposed model are demonstrated in multiple simulation studies and implemented to analyze public opinion on homeownership as the best long-term investment in the United States following the Great Recession.

This study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for precise information categorization and decision support across various management departments.

This study leverages the ALBERT–TextCNN algorithm to determine the appropriate department for managing online appeals. ALBERT is selected for its advanced dynamic word representation capabilities, rooted in a multi-layer bidirectional transformer architecture and enriched text vector representation. TextCNN is integrated to facilitate the development of multi-label classification models.

Comparative experiments demonstrate the effectiveness of the proposed approach and its significant superiority over traditional classification methods in terms of accuracy.

The original contribution of this study lies in its utilization of the ALBERT–TextCNN algorithm for the classification of online appeals, resulting in a substantial improvement in accuracy. This research offers valuable insights for management departments, enabling enhanced understanding of public appeals and fostering more scientifically grounded and effective decision-making processes.

While corporate social responsibility (CSR) has been gaining support for the role it plays in employee outcomes, such as organizational identification (OID), the view of CSR from a social identity perspective is underdeveloped. This conceptual chapter explores the role of social identity processes grounded in organizational justice to develop a model of CSR attributions and the moderating role these attributions play in organizational member outcomes. CSR is understood as the relational processes happening with stakeholders, and these relationships engage specific organizational identity orientations. The social identity process flows from there, resulting in CSR attributions including strategic, relational, and virtuous. Using social identity, organizational identity, and organizational justice, this chapter makes two specific contributions: a CSR attribution typology grounded in organizational justice and the moderating impact of these attributions between activated justice dimensions and resulting organizational member outcomes.