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A good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to propose a robust credit scoring system by leveraging latent information embedded in the telecom subscriber relation network based on multi-source data sources, including telecom inner data, online app usage, and offline consumption footprint.

Rooting from network science, the relation network model and singular value decomposition are integrated to infer different subscriber subgroups. Employing the results of network inference, the paper proposed a network-aware credit scoring system to predict the continuous credit scores by implementing several state-of-art techniques, i.e. multivariate linear regression, random forest regression, support vector regression, multilayer perceptron, and a deep learning algorithm. The authors use a data set consisting of 926 users of a Chinese major telecom operator within one month of 2018 to verify the proposed approach.

The distribution of telecom subscriber relation network follows a power-law function instead of the Gaussian function previously thought. This network-aware inference divides the subscriber population into a connected subgroup and a discrete subgroup. Besides, the findings demonstrate that the network-aware decision support system achieves better and more accurate prediction performance. In particular, the results show that our approach considering stochastic equivalence reveals that the forecasting error of the connected-subgroup model is significantly reduced by 7.89–25.64% as compared to the benchmark. Deep learning performs the best which might indicate that a non-linear relationship exists between telecom subscribers' credit scores and their multi-channel behaviours.

This paper contributes to the existing literature on business intelligence analytics and continuous credit scoring by incorporating latent information of the relation network and external information from multi-source data (e.g. online app usage and offline consumption footprint). Also, the authors have proposed a power-law distribution-based network-aware decision support system to reinforce the prediction performance of individual telecom subscribers' credit scoring for the telecom marketing domain.

The objective of this paper is to propose a consumer-behavior-based intelligence (CBBI) model to identify market structure so as to monitor product competition. Competitive intelligence extracted from Chinese e-business clickstream data is exploited to examine the relevance of consumers' heterogeneous behavioral feedback, namely, click, tag-into-favorite, time-of-browsing, add-into-cart, and remove-from-cart, to visualize the competitive product market structure and to predict product-level sales.

Our proposed CBBI model consists of visualization and prediction, which explore e-business clickstream data. We conduct the visualization and segmentation of market structure in the form of a perceptual map by employing K-means clustering algorithm and multidimensional scaling technique. Concurrently, we developed an updated Bayesian linear regression (BLR) to predict product-level sales by considering consumers' heterogeneous feedback. Our updated BLR specifically integrated the estimated knowledge of the previous periods to verify whether product sales are period-dependent due to the consumer memory effect in e-commerce, improving the conventional BLR of diffuse prior distribution setup in terms of mean absolute error (MAE) and root mean squared error (RMSE).

Considering the performance of consumers' heterogeneous actions, the present research visualized three different segments of the competitive market structure in a perceptual map, and its horizontal axis is shown as a signal of the ascending trend of product sales. The previous five-day period was ascertained to be the best size of a time window for the consumer memory effect on product sales prediction. This hypothesis is supported by the concept that product sales are period-dependent. The results of the proposed updated BLR indicate that consumer tag-into-favorite, add-into-cart, and remove-from-cart feedback have positive impacts on product-level sales while click and time-of-browsing have the opposite effect.

While the identified competitive product market structure elaborates consumer heterogeneous feedback toward alternative product choices, this paper contributes by extending those homogeneous consumer preferences-related marketing studies. The perceptual map's configuration in respect to period-dependent product sales facilitates the effective inclusion of consumer behavior application in product sales prediction research in e-commerce. This paper helps sellers and retailers better comprehend the impacts of heterogeneous feedback and the consumer memory effect on the degree of competition in the form of product sales. The research results also offer a managerial implication about shaping the competitive edge by conducting different product management strategies in e-commerce platforms.

The purpose of this paper is to identify potential competitive relationships among brands by analyzing the dynamic clicking behavior of consumers.

Consumer sequential online click data, collected from JD.com, is used to analyze the dynamic competitive relationship between brands. It is found that the competition intensity across categories of products can differ considerably. Consumers exhibit big differences in purchasing time of durable-like goods, that is, the purchasing probability of such products changes considerably over time. The local polynomial regression model (LPRM) is used to analyze the relationship between brand competition of durable-like goods and the purchasing probability of a particular brand.

The statistical results of collective behaviors show that there is a 90/10 rule for the category durable-like goods, implying that ten percent of the brands account for 90 percent market share in terms of both clicking and purchasing behavior. The dynamic brand cognitive process of impulsive consumers displays an inverted V shape, while cautious consumers display a double V shaped cognitive process. The dynamic consumers’ cognition illustrates that when the brands capture a half of the click volume, the brands’ competitiveness reaches to its peak and makes no significant different from brands accounting for 100 percent of the click volume in terms of the purchasing probability.

There are some limitations to the research, including the limitations imposed by the data set. One of the most serious problems in the data set is that the collected click-stream is desensitized severely, restricting the richness of the conclusions of this study. Second, the data set consists of many other consumer behavioral data, but only the consumer’s clicking behavior is analyzed in this study. Therefore, in future research, the parameters brand browsing by consumers and the time of browsing in each brand should be added as indicators of brand competitive intensity.

The authors study brand competitiveness by analyzing the relationship between the click rate and the purchase likelihood of individual brands for durable-like products. When the brand competitiveness is less than 50 percent, consumers tend to seek a variety of new brands, and their purchase likelihood is positively correlated with the brand competitiveness. Once consumers learn about a particular brand excessively among all other brands at a period of time, the purchase likelihood of its products decreases due to the thinner consumer’s short-term loyalty the brand. Till the brand competitiveness runs up to 100 percent, consumers are most likely to purchase a brand and its product. That indicates brand competitiveness maintain 50 percent of the whole market is most efficient to be profitable, and the performance of costing more to improve the brand competitiveness might make no difference.

There are many studies on brand competition, but most of these research works analyze the brand’s marketing strategy from the perspective of the company. The limitation of this research is that the data are historical and failure to reflect time-variant competition. Some researchers have studied brand competition through consumer behavior, but the shortcoming of these studies is that it does not consider sequentiality of consumer behavior as this study does. Therefore, this study contributes to the literature by using consumers’ sequential clicking behavior and expands the perspective of brand competition research from the angle of consumers. Simultaneously, this paper uses the LPRM to analyze the relationship between consumer clicking behavior and brand competition for the first time, and expands the methodology accordingly.

Purchase conversion prediction aims to improve user experience and convert visitors into real buyers to drive sales of firms; however, the total conversion rate is low, especially for e-retailers. To date, little is known about how e-retailers can scientifically detect users' intents within a purchase conversion funnel during their ongoing sessions and strategically optimize real-time marketing tactics corresponding to dynamic intent states. This study mainly aims to detect a real-time state of the conversion funnel based on graph theory, which refers to a five-class classification problem in the overt real-time choice decisions (RTCDs)—click, tag-to-wishlist, add-to-cart, remove-from-cart and purchase—during an ongoing session.

The authors propose a novel graph-theoretic framework to detect different states of the conversion funnel by identifying a user's unobserved mindset revealed from their navigation process graph, namely clickstream graph. First, the raw clickstream data are identified into individual sessions based on a 30-min time-out heuristic approach. Then, the authors convert each session into a sequence of temporal item-level clickstream graphs and conduct a temporal graph feature engineering according to the basic, single-, dyadic- and triadic-node and global characteristics. Furthermore, the synthetic minority oversampling technique is adopted to address with the problem of classifying imbalanced data. Finally, the authors train and test the proposed approach with several popular artificial intelligence algorithms.

The graph-theoretic approach validates that users' latent intent states within the conversion funnel can be interpreted as time-varying natures of their online graph footprints. In particular, the experimental results indicate that the graph-theoretic feature-oriented models achieve a substantial improvement of over 27% in line with the macro-average and micro-average area under the precision-recall curve, as compared to the conventional ones. In addition, the top five informative graph features for RTCDs are found to be Transitivity, Edge, Node, Degree and Reciprocity. In view of interpretability, the basic, single-, dyadic- and triadic-node and global characteristics of clickstream graphs have their specific advantages.

The findings suggest that the temporal graph-theoretic approach can form an efficient and powerful AI-based real-time intent detecting decision-support system. Different levels of graph features have their specific interpretability on RTCDs from the perspectives of consumer behavior and psychology, which provides a theoretical basis for the design of computer information systems and the optimization of the ongoing session intervention or recommendation in e-commerce.

To the best of the authors' knowledge, this is the first study to apply clickstream graphs and real-time decision choices in conversion prediction and detection. Most studies have only meditated on a binary classification problem, while this study applies a graph-theoretic approach in a five-class classification problem. In addition, this study constructs temporal item-level graphs to represent the original structure of clickstream session data based on graph theory. The time-varying characteristics of the proposed approach enhance the performance of purchase conversion detection during an ongoing session.

This study examines the operational and relational governances as antecedents of cooperation commitment in buyer–supplier exchanges. It also assesses the impact of cooperation commitment on operational performance.

Path analysis was performed on the data collected from manufacturers.

The results of this study show that both operational and relational governances exert impact on cooperation commitment, which, in turn, is associated with operational performance improvement.

First, this is the first study employing the reciprocity theory to theorize the conceptual framework of the governance antecedents of cooperation commitment and operations excellence effect. Second, the study highlights how the research framework can enrich the reciprocity theory in exploring the mechanisms of the operational and relational governances of buyer–supplier exchanges and their impact on the commitment to the cooperation. Third, this study extends the reciprocity theory to examine in detail how cooperation commitment exerts impact on the operational performance.

The purpose of this paper is to examine the effectiveness of illegal insider trading enforcement in China by focusing, among other things, on the Chinese Securities Regulatory Commission's (CSRC) enforcement actions in the period 1993‐2006.

This paper discusses the CSRC's enforcement policies and practices of insider trading regulation, based upon administrative and judicial cases, face‐to‐face interviews with regulators, and policy documents.

A major finding of the study is the paucity of insider trading cases and the lack of convictions for insider trading offences in China. The campaign against securities offences did not actually come with the stricter enforcement of insider trading laws. A primary challenge in the insider trading regulation comes from the fact that most insider trading cases involve high‐ranking government and party officials. The CSRC lacks the power to directly administer discipline and penalties on government officials and party cadres for insider trading offences.

It is recommended that the CSRC be given more power, more resources and more trained regulators to detect and address insider trading activities. It is also recommended that the CSRC improve its surveillance capabilities by fully utilizing sophisticated computer surveillance software systems, by improving inter‐agency and inter‐market information‐sharing, and by cooperating with other countries' regulators and participating in the ISG's database to detect possible international insider trading.

The paper will be of interest to researchers in the field of financial crime and securities regulation. Regulators, the private sector and government departments will also benefit from an analysis of Chinese insider trading enforcement cases. This paper also suggests better strategies for dealing with insider trading offences in China. A fair and orderly market is crucial for investors in the Chinese market.

This study aims to investigate the relationship between corporate social responsibility (CSR) disclosure and financial performance (FP) of Islamic banking sector from a developing country perspective. It also explores the present status of CSR activities performing by the listed Islamic banks (IBs) of Bangladesh.

The secondary data from seven IBs’ annual reports for the years 2009–2018 are taken to obtain substantial measures of CSR activities. A corporate social responsibility disclosure index is constructed based on disclosure status on nine dimensions and 75 items as per the Accounting and Auditing Organization for Islamic Financial Institutions standards. To find the association between CSR disclosures and profitability, panel regression analysis has been performed.

The result indicates that CSR disclosures have a significant and negative relation with FP (return on assets) of IBs. It also suggests the expansion of CSR practices and the communicative CSR reporting of IBs, as an ethical identity, toward the stakeholders and society.

First, the samples used in this study are limited to IBs as ethical identities in Bangladesh. Second, the length of a time frame as the practice of CSR activities and its reporting is still ineffective following the enforcement of the central bank directive in 2008. Another limitation is that the study used a subjective measure, content analysis, of CSR activities that was self-reported disclosures, which may creep some biasness.

The practical involvement of this research includes the assistance for policy development regarding better understanding of expansion of CSR practices and trustworthiness of CSR reporting by the Islamic banking segments in developing country context. Future researchers can get a glimpse of what reputational impact CSR initiatives really have on consumers and investors, considering CSR activities as an indicator of greater transparency and honesty in operations and financial reporting.

This study makes an important contribution to the academic literature on CSR communication from developing country context where CSR activities are supported under Islamic banking system. In addition, its examination of the legitimacy of CSR disclosures elaborates the social obligations of corporate entities to their stakeholders and society.

This paper aims to collect the energy consumption data and carry out energy consumption analysis of chemical enterprises, which is helpful to grasp the working conditions of each equipment accurately and to perfect the demand side management (DSM) for the user in the terminal.

The paper proposes a load monitoring system of chemical enterprises to collect the energy consumption data and carry out energy consumption analysis. An Elman neural network based on sparrow search algorithm is proposed to predict the power consumption change and distribution trend of enterprises in the future production cycle. The calculation efficiency and prediction accuracy have been significantly improved.

The paper analyzes the energy saving effect of energy efficiency management as well as “avoiding peak and filling valley” measures, and reasonable control requirements and assumed conditions are put forward to study the operability of enterprise energy saving measures from the DSM.

Because of the chosen enterprise data, the prediction accuracy needs to be further improved. Therefore, researchers are encouraged to test the proposed methodology further.

The paper includes implications for the development of energy consumption analysis and load forecasting of chemical enterprises and perfects the DSM for the user.

This paper fulfills an identified need to study how to forecast the power load and improve the management efficiency of energy consumption.

This research aims to explore the perspectives of the key actors in the New Zealand construction industry towards BIM adoption. Specifically, four themes are examined, including what BIM is; BIM knowledge and understanding; benefits of BIM adoption; and challenges/barriers to BIM adoption.

A qualitative approach using 21 semi-structured interviews with industry experts was adopted.

The results raise a question concerning whether the New Zealand construction industry needs a unique definition of BIM to achieve a clear and consistent understanding amongst construction practitioners. It was found out that most of the construction practitioners in New Zealand are not well aware of BIM, especially the contractors, QSs, supply chain companies and the SMEs. Fourteen potential benefits and ten barriers/challenges to BIM adoption were identified. Individually, time-saving was considered as the most benefit of BIM adoption while BIM understanding was suggested as the most significant barrier by all the interviewees.

The research provides valuable insights into BIM understanding as well as recommendations regarding BIM adoption in New Zealand. The results could be considered baseline information for the companies and government to have effective strategies towards BIM adoption. Furthermore, it confirms that characteristics such as benefits and barriers to BIM adoption amongst different countries could be similar. Therefore, it could be useful to analyse the studies, strategies and practices of the pioneer countries in BIM adoption for the implementation.