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This study aims to provide an in-depth understanding of investors’ cognition and decision-making process with regard to internet financial products. The objective is to effectively guide users’ rational investments.

First, based on grounded theory, this study develops a tool for measuring users’ perceived value (PV) of internet financial products via in-depth interviews. Then, after comprehensively considering users’ environmental, individual and psychological characteristics, this study proposes a theoretical model of internet financial product investment decisions based on the PV of users. Finally, an empirical study is conducted on 693 valid sample data from e-commerce and online banking financial platforms.

The empirical results suggest that network externalities influence users’ financial behavior by herding (HE) (imitating others and discounting their own information) and PV. PV and HE are key factors in users’ investment decisions with regard to internet financial products. Moreover, users’ self-efficacy (SE) and platform type play moderate roles in the influence mechanism.

The research conclusions provide valuable references for designing financial products and establishing regulatory rules, which will help the internet financial industry to grow soundly and innovatively.

This study uncovers the mediating effect of HE and PV between network externalities and users’ investment intentions in the context of internet financial products. In addition, the moderating effect of users’ SE and platform types is revealed.

The purpose of this paper is to alleviate the problem of poor robustness and over-fitting caused by large-scale data in collaborative filtering recommendation algorithms.

Interpreting user behavior from the probabilistic perspective of hidden variables is helpful to improve robustness and over-fitting problems. Constructing a recommendation network by variational inference can effectively solve the complex distribution calculation in the probabilistic recommendation model. Based on the aforementioned analysis, this paper uses variational auto-encoder to construct a generating network, which can restore user-rating data to solve the problem of poor robustness and over-fitting caused by large-scale data. Meanwhile, for the existing KL-vanishing problem in the variational inference deep learning model, this paper optimizes the model by the KL annealing and Free Bits methods.

The effect of the basic model is considerably improved after using the KL annealing or Free Bits method to solve KL vanishing. The proposed models evidently perform worse than competitors on small data sets, such as MovieLens 1 M. By contrast, they have better effects on large data sets such as MovieLens 10 M and MovieLens 20 M.

This paper presents the usage of the variational inference model for collaborative filtering recommendation and introduces the KL annealing and Free Bits methods to improve the basic model effect. Because the variational inference training denotes the probability distribution of the hidden vector, the problem of poor robustness and overfitting is alleviated. When the amount of data is relatively large in the actual application scenario, the probability distribution of the fitted actual data can better represent the user and the item. Therefore, using variational inference for collaborative filtering recommendation is of practical value.

The purpose of this paper is to establish criteria for evaluating strategic partners in a network of logistics service providers (LSPs) to show how analytical network process (ANP) can be used to identify the weights of these criteria on a case-specific basis, and to investigate whether the ANP model can be used as a starting point to evaluate strategic partners for other LSP networks.

Based on a literature review of vertical cooperation, the authors develop an overview of criteria for the evaluation of partners in a network of LSPs. The authors then apply ANP at LSP1 to validate the criteria, identify weights for these criteria and to validate model outcomes. Furthermore, the authors investigate whether the ANP model developed for LSP1 can be applied to another LSP with similar characteristics (LSP2). In-depth interviews are used to draw conclusions on the modeling approach and the model outcomes.

The research shows that evaluation criteria for partners in vertical partnerships between shippers and LSPs are applicable to LSP partners in horizontal partnership networks. The ANP model with criteria weights provides a good starting point for LSPs to customize the evaluation framework according to their specific needs or operating environments.

Limited research is available on evaluating LSP partners in horizontal partnerships. To the best of the authors’ knowledge, this paper is the first to bring forward horizontal LSP partner evaluation criteria to develop an ANP model for LSP partner evaluation and to apply this to two cases, and to provide a starting point for evaluating partners in similar horizontal LSP networks.

The main objectives of collective forest tenure reform in China are to stimulate rural households to invest in forestry management, protect the ecosystem and improve their livelihood. By constructing the unbalanced panel data of household investment, this study discusses the dynamic changes and influencing factors of household investment, which will lay a foundation for further research and provide a reference for decision-making.

Based on 3,500 samples from rural households in the provinces of Fujian, Hunan, Yunnan, Shaanxi, Jiangxi, Gansu, and Liaoning collected during field investigations from 2010 to 2015, this study conducts an empirical analysis of the household investment in forestry management and its factors with nonbalanced panels.

According to the analysis, the average investment in forestry management per household from 2010 to 2015 fluctuates greatly; the age of the householder, increased forestry area, subsidies, joining professional cooperatives, and forest tenure mortgage show positive effects on achieving the objectives.

The discussions are drawn from the study that supporting policies such as the forest tenure transfer system, professional cooperatives, financial services and subsidies should be further improved to sustain a positive in the forestry industry.

The first step toward developing an automated construction workers performance monitoring system is to initially establish a complete and competent activity recognition solution, which is still lacking. This study aims to propose a novel approach of using labor physiological data collected through wearable sensors as means of remote and automatic activity recognition.

A pilot study is conducted against three pre-fabrication stone construction workers throughout three full working shifts to test the ability of automatically recognizing the type of activities they perform in-site through their lively measured physiological signals (i.e. blood volume pulse, respiration rate, heart rate, galvanic skin response and skin temperature). The physiological data are broadcasted from wearable sensors to a tablet application developed for this particular purpose, and are therefore used to train and assess the performance of various machine-learning classifiers.

A promising result of up to 88% accuracy level for activity recognition was achieved by using an artificial neural network classifier. Nonetheless, special care needs to be taken for some activities that evoke similar physiological patterns. It is expected that blending this method with other currently developed camera-based or kinetic-based methods would yield higher activity recognition accuracy levels.

The proposed method complements previously proposed labor tracking methods that focused on monitoring labor trajectories and postures, by using additional rich source of information from labors physiology, for real-time and remote activity recognition. Ultimately, this paves for an automated and comprehensive solution with which construction managers could monitor, control and collect rich real-time data about workers performance remotely.

The literature review aims to facilitate a broader understanding of on-demand service platform operations management and proposes potential research directions for scholars.

This study searches four databases for relevant literature on on-demand service platform operations management and selects 72 papers for this review. According to the research context, the literature can be divided into research on “a single platform” and research on “multiple platforms”. According to the research methods, the literature can be classified into “Mathematical Models”, “Empirical Studies”, “Multiple Methods” and “Literature Review”. Through comparative analysis, we identify research gaps and propose five future research agendas.

This paper proposes five research agendas for future research on on-demand service platform operations management. First, research can be done to combine classic research problems in the field of operations management with platform characteristics. Second, both the dynamic and steady-state issues of on-demand service platforms can be further explored. Third, research employing mathematical models and empirical analysis simultaneously can be more fruitful. Fourth, more research efforts on the various interactions among two or more platforms can be pursued. Last but not least, it is worthwhile to examine new models and paths that have emerged during the latest development of the platform economy.

Through categorizing the literature into two research contexts as well as classifying it according to four research methods, this article clearly shows the research progresses made so far in on-demand service platform operations management and provides future research directions.

The purpose of this study is to review and synthesize the literature on in-app advertising, identify gaps and propose future research directions.

The authors use a systematic literature review (SLR) approach, following the PRISMA guidelines, to investigate the current state of research in in-app advertising. The study uses 44 shortlisted articles from the Scopus and Web of Science databases. Using the Theory-Context-Characteristics-Methodology (TCCM) framework, the authors analyze the gaps in theory, context, characteristics and methods.

Using thematic analysis, the authors identify five main themes in the in-app advertising literature, namely, ad platform optimization; mobile app user psychology and behavior; ad effectiveness; ad fraud; and security, privacy and other user concerns. The findings show the need for empirical research, with a strong theoretical foundation in emerging ad formats of in-app advertising, user behavior and buy-side of in-app advertising.

This is a maiden study to conduct a domain-based SLR in the emerging field of in-app advertising using the TCCM framework. The authors highlight the key differences between in-app advertising and mobile web advertising. The authors propose theories in the advertising field that could be used in future empirical studies of in-app advertising.

El propósito de esta investigación es revisar y sintetizar la literatura sobre la publicidad en Apps, identificar lagunas y proponer futuras direcciones de investigación.

Utilizamos un enfoque de revisión sistemática de la literatura, siguiendo las directrices PRISMA, para investigar el estado actual de la investigación en publicidad en aplicaciones. El estudio utiliza 44 artículos preseleccionados de las bases de datos Scopus y Web of Science (WoS). Utilizando el marco Teoría-Contexto-Características-Metodología (TCCM), analizamos las lagunas en teoría, contexto, características y métodos.

Mediante un análisis temático, identificamos cinco temas principales en la literatura sobre publicidad en aplicaciones, a saber: optimización de plataformas publicitarias; psicología y comportamiento de los usuarios de aplicaciones móviles; eficacia publicitaria; fraude publicitario; seguridad, privacidad y otras preocupaciones de los usuarios. Nuestros hallazgos muestran la necesidad de investigación empírica, con una sólida base teórica en los formatos publicitarios emergentes de la publicidad en Apps, el comportamiento del usuario y el buy-side de la publicidad en Apps.

Se trata de un estudio pionero para realizar una revisión sistemática de la literatura basada en el dominio en el campo emergente de la publicidad en Apps utilizando el marco TCCM. Destacamos las principales diferencias entre la publicidad en aplicaciones y la publicidad en la web para móviles. Proponemos teorías en el campo de la publicidad que podrían utilizarse en futuros estudios empíricos sobre la publicidad en Apps.

本研究旨在回顾和总结有关应用内广告的文献, 找出差距并提出未来的研究方向。

我们采用系统性文献综述方法, 遵循 PRISMA 指南, 调查应用内广告的研究现状。研究使用了 Scopus 和 Web of Science (WoS) 数据库中的 44 篇入围文章。利用理论-背景-特征-方法（TCCM）框架, 我们分析了理论、背景、特征和方法方面的差距。

通过主题分析, 我们确定了应用内广告文献的五大主题, 即广告平台优化; 移动应用用户心理和行为; 广告效果; 广告欺诈; 安全、隐私和其他用户关注点。我们的研究结果表明, 有必要在应用内广告的新兴广告形式、用户行为和应用内广告买方等方面开展实证研究, 并奠定坚实的理论基础。

这是一项首次使用 TCCM 框架对新兴的应用内广告领域进行基于领域的系统性文献综述的研究。我们强调了应用内广告与移动网络广告的主要区别。我们提出了广告领域的理论, 可用于未来的应用内广告实证研究。

The field of website quality evaluation attracts the interest of a range of disciplines, each bringing its own particular perspective to bear. This study aims to identify the main characteristics – methods, techniques and tools – of the instruments of evaluation described in this literature, with a specific concern for the factors analysed, and based on these, a multipurpose model is proposed for the development of new comprehensive instruments.

Following a systematic bibliographic review, 305 publications on website quality are examined, the field's leading authors, their disciplines of origin and the sectors to which the websites being assessed belong are identified, and the methods they employ characterised.

Evaluations of website quality tend to be conducted with one of three primary focuses: strategic, functional or experiential. The technique of expert analysis predominates over user studies and most of the instruments examined classify the characteristics to be evaluated – for example, usability and content – into factors that operate at different levels, albeit that there is little agreement on the names used in referring to them.

Based on the factors detected in the 50 most cited works, a model is developed that classifies these factors into 13 dimensions and more than 120 general parameters. The resulting model provides a comprehensive evaluation framework and constitutes an initial step towards a shared conceptualization of the discipline of website quality.

This study aims to measure the effects of moderation by age and income in mobile payment systems' intended use as predictors of performance expectation, effort expectation, social influence, risk and perceived costs.

This study is based on a survey that generated a sample of 1,742 Brazilian users that responded to the measurement scale. The research data were analyzed using the partial least squares structural equation model.

All proposed latent variables were significant, with income positively moderating the performance expectation and negatively moderating the perceived cost and perceived risk. In addition, age positively moderates performance expectation and negatively moderates cost perception.

The findings evolved previous literature by understanding moderating effects that make it possible for companies operating in mobile payments to generate segmented communication and engagement plans for users of different income and age brackets.

This study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.

This study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.

Six clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.

This study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.