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The performance of behavioral targeting (BT) mainly relies on the effectiveness of user classification since advertisers always want to target their advertisements to the most relevant users. In this paper, the authors frame the BT as a user classification problem and describe a machine learning–based approach for solving it.

To perform such a study, two major research questions are investigated: the first question is how to represent a user’s online behavior. A good representation strategy should be able to effectively classify users based on their online activities. The second question is how different representation strategies affect the targeting performance. The authors propose three user behavior representation methods and compare them empirically using the area under the receiver operating characteristic curve (AUC) as a performance measure.

The experimental results indicate that ad campaign effectiveness can be significantly improved by combining user search queries, clicked URLs and clicked ads as a user profile. In addition, the authors also explore the temporal aspect of user behavior history by investigating the effect of history length on targeting performance. The authors note that an improvement of approximately 6.5% in AUC is achieved when user history is extended from 1 day to 14 days, which is substantial in targeting performance.

This paper confirms the effectiveness of BT on user classification and provides a validation of BT for Internet advertising.

This chapter provides an overview of the strategic implications of chatbot use and implementation, including potential applications in marketing, and factors affecting customer acceptance. After presenting a brief history and a classification of conversational artificial intelligence (AI) and chatbots, the authors provide an in-depth review at the crossroads between marketing, business, and human–computer interaction, to outline the main factors that drive users' perceptions and acceptance of chatbots. In particular, the authors describe technology-related factors and chatbot design characteristics, such as anthropomorphism, gender, identity, and emotional design; context-related factors, such as the product type, task orientation, and consumption contexts; and users-related factors such as sociodemographic and psychographic characteristics. Next, the authors detail the strategic importance of chatbots in the field of marketing and their impact on consumers' perceived service quality, satisfaction, trust, and loyalty. After discussing the ethical implications related to chatbots implementation, the authors conclude with an exploration of future opportunities and potential strategies related to new generative AI technologies, such as ChatGPT. Throughout the chapter, the authors offer theoretical insights and practical implications for incorporating conversational AI into marketing strategies.

Prefabricated building (PB) has been a pivotal force in advancing global building industrialization and sustainability. However, the PB supply chain operation faces significant challenges of exhausting negotiations, poor communication and imperfect information, representing high transaction costs (TCs). Existing literature inadequately addresses governance behaviors to mitigate TCs. This study aims to explore PB supply chain inefficiencies through the lens of TC theory, examining the nuanced relationships between hybrid governance behaviors and TCs and exploring effective governance strategies.

Based on the theoretical frameworks of governance behavior and TCs, this study employed semi-structured interviews and questionnaire surveys with PB experts in Anhui, China. Subsequently, integrated backpropagation neural network and ordered logistic regression analyses were conducted to identify critical governance behaviors and explore boundaries for TCs reduction.

TCs of the PB supply chain are elevated (1) from communication and coordination; (2) during the construction and approval stages. Investigation of how governance behaviors influence the TCs indicated that (1) enterprises exert more influence than local governments; (2) governance effectiveness in the transaction and transaction environment dimensions outweighs stakeholder influence and (3) functional TCs exist in PB, associated with component manufacturing, PB contract negotiation and learning cost.

This study extends understanding of TCs in PB by providing nuanced insights into the nature and timing of TCs and elucidates how governance structures shape TCs. Functional TCs intrinsic to PB were identified when exploring the optimization boundaries. These insights equip local governments and enterprises with actionable knowledge to prioritize effective governance behaviors and measures.

During the cooperation process between prefabricated building construction enterprises (PBCEs) and Internet platforms (IPs), the sentiments of both parties influence their behavioral strategies. They are the key to improving the informatization and operational efficiency of the prefabricated building industry chain (PBIC).

This paper introduces mental accounting theory and rank-dependent expected utility theory to construct the MA-RDEU game model, exploring the evolutionary mechanism between sentiment and behavioral strategies of PBCEs and IPs.

The study indicates that (1) a mixed strategy equilibrium can be achieved when both parties have no sentiments. (2) PBCEs and IPs are more likely to achieve an optimal equilibrium for cooperation if the latter is optimistic. In contrast, pessimism may lead both parties to prioritize self-interest when only one party has a sentiment. (3) The combined impact of sentiments and behavioral strategies on decision-making is significant: the influence of sentiments from PBCEs or IPs on the optimal strategy for achieving cooperation is contingent upon the behavioral strategies of the other party; different behavioral strategies of IPs or PBCEs can have varying effects on sentiments when both parties have sentiments. (4) The influence of external factors on the sentiments and behavior strategies of PBCEs and IPs is apparent. PBCEs and IPs should concurrently consider the combined influence of external factors and sentiments to contribute to the realization of cooperation between the two parties. Additionally, government supervision is an effective means to restrain “free-riding” behavior.

Finally, based on the above conclusions, the paper proposes measures to improve the construction of service-oriented IPs and establish a mechanism for monitoring and adjusting risk sentiments. Meanwhile, this paper also indicates that under the combined effect of the government, PBCEs and IPs, the influence of external factors on sentiments can be maintained within a controllable scope and the risks of uncertainty can be mitigated.

This paper aims to investigate the volatility transmission across stocks, gold and crude oil markets before and during the novel coronavirus (COVID-19) crisis.

A multivariate vector autoregression (VAR)-Baba, Engle, Kraft and Kroner generalized autoregressive conditional heteroskedasticity model (BEKK-GARCH) is used to assess volatility transmission across the examined markets. The sample is divided as follows. The first period ranging from 02/01/2019 to 10/03/2020 defines the pre-COVID-19 crisis. The second period is from 11/03/2020 to 05/10/2020, representing the COVID-19 crisis period. Then, a robustness test is used using exponential GARCH models after including an exogenous variable capturing the growth of COVID-19 confirmed death cases worldwide with the aim to test the accuracy of the VAR-BEKK-GARCH estimated results.

Results indicate that the interconnectedness among the examined market has been intensified during the COVID-19 crisis, proving the lack of hedging opportunities. It is also found that stocks and Gold markets lead the crude oil market especially during the COVID-19 crisis, which explains the freefall of the crude oil price during the health crisis. Similarly, results show that Gold is most likely to act as a diversifier rather than a hedging tool during the current health crisis.

Although the recent studies in the field focused on analyzing the relationships between different markets during the first quarter of 2020, this study considers a larger data set with the aim to assess the volatility transmission across the examined international markets Amid the COVID-19 crisis, while it shows the most significant impact on various financial markets compared to other diseases.

This research is aimed to mainly be applicable to expediting engineering projects, uses the method of inverse optimization and the double-layer nested genetic algorithm combined with nonlinear programming algorithm, study how to schedule the number of labor in each process at the minimum cost to achieve an extremely short construction period goal.

The method of inverse optimization is mainly used in this study. In the first phase, establish a positive optimization model, according to the existing labor constraints, aiming at the shortest construction period. In the second phase, under the condition that the expected shortest construction period is known, on the basis of the positive optimization model, the inverse optimization method is used to establish the inverse optimization model aiming at the minimum change of the number of workers, and finally the optimal labor allocation scheme that meets the conditions is obtained. Finally, use algorithm to solve and prove with a case.

The case study shows that this method can effectively achieve the extremely short duration goal of the engineering project at the minimum cost, and provide the basis for the decision-making of the engineering project.

The contribution of this paper to the existing knowledge is to carry out a preliminary study on the relatively blank field of the current engineering project with a very short construction period, and provide a path for the vast number of engineering projects with strict requirements on the construction period to achieve a very short construction period, and apply the inverse optimization method to the engineering field. Furthermore, a double-nested genetic algorithm and nonlinear programming algorithm are designed. It can effectively solve various optimization problems.