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The purpose of this paper is to use systemic thinking to explain and predict the cost of logistics outsourcing, and to devise policies to minimize the cost of risk.

A method of system dynamics is adopted to capture the dynamic interaction of logistics outsourcing systems and to analyze the impact of some factors in the system on policy decisions over a long‐term horizon.

This paper illustrates the internal mechanism of the logistics outsourcing cost of risk systems by virtue of system dynamic principles, to develop a system dynamics model, and to give a quite detailed description of how the model could work.

The results of the simulation analysis provide useful information for logistics outsourcing risk managers.

This paper contributes to the discussion on the use of system dynamics for studying logistics outsourcing cost of risk.

This paper seeks to challenge the traditional wisdom that sheds light upon sequential entry modes in developed countries by exploring the dynamic entry mode choice in sequential foreign direct investment (FDI) in emerging economies.

A review of the literature on the entry mode choice is undertaken. Based on analysing two related theories consisting of the knowledge‐based theory of the firm and organizational learning theory, entry mode choices in sequential FDI in emerging economies are investigated using both an internationalisation process model and the capability‐developing perspective, and exclusive propositions are put forward accordingly. Then, these propositions are tested on the context of China with the methodology of paired‐samples t‐tests.

Based on macro‐level longitudinal data in China from 1979 to 2005, the choice of entry mode in sequential FDI in emerging economies is inconsistent with the capability‐developing theory of the firm, but is consistent with the international process model.

This study provides four practical implications. First, managers intending to invest abroad need to consider the cost and return of a specific entry mode. Second, knowledge about host markets has a more important effect on entry mode choice in emerging markets than MNCs' internal organizational capabilities. Third, MNCs adopt sequential investment in emerging economies, in which they adopt joint ventures in earlier entries and then shift to green‐field investment in later entries. Fourth, experiential learning, which consists of learning about host markets and local partners' skills, is emphasized in sequentially entering emerging markets.

This paper expands the research scope of previous studies that either explore a static choice of entry mode in foreign markets or only examine the entry mode choice in sequential FDI in developed countries. Taking into consideration the dynamic choice of entry modes, the paper studies sequential FDI in emerging economies, which throws light upon theoretical analysis of sequential FDI in China, and which has practical implications for foreign firms that are interested in China and planning to enter China's markets.

The purpose of this paper is to consider three factors, namely, intra-week demand fluctuations, interrelationship between the number of robots and order scheduling and conflicting objectives (i.e. cost minimization and customer satisfaction maximization), to optimize the robot logistics system.

The number of robots and the sequence of delivery orders are first optimized using the heuristic algorithm NSGACoDEM, which is designed using genetic algorithm and composite difference evolution. The superiority of this method is then confirmed by a case study of a four-star grade hotel in South Korea and several comparative experiments.

Two performance metrics reveal the superior performance of the proposed approach compared to other baseline approaches. Results of comparative experiments found that the consideration of three influencing factors in the operation design of a robot logistic system can effectively balance cost and customer satisfaction over the course of a week in hotel operation and optimize robot scheduling flexibility.

The results of this study reveal that numerous factors (e.g. intra-week demand fluctuations) can optimize the performance efficiency of robots. The proposed algorithm can be used by hotels to overcome the influence of intra-week demand fluctuations on robot scheduling flexibility effectively and thereby enhance work efficiency.

The design of a novel algorithm in this study entails enhancing the current robot logistics system. This algorithm can successfully manage cost and customer satisfaction during off-seasons and peak seasons in the hotel industry while offering diversified schemes to various types of hotels.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

Data has become a factor of production. This occurs when history enters the era of big data, in which technologies such as artificial intelligence, cloud computing and blockchain are used to collect, manipulate, mine and process data. Data is a special product of labor, a sub-derivative of other production factors.

The data factor has a dual attribute: being physical (technical) and social. The social attribute of the data factor can not only materialize the technical attribute but also amplify it. In other words, the data has a multiplication effect on the allocation efficiency of other production factors. The social attribute of the data is brought out via the technical attribute as the medium. From a technical perspective, this medium is strongly adhesive, and after being bonded with other factors of production, it will only lead to a physical reaction and not change the nature of other factors.

However, once these two attributes interact with each other, especially when data is combined with capital, the most adhesive factor in the market economy, a series of new social relations will then be produced based on the technical attribute, resulting in significant adjustments in social relations, involving both positive and negative externalities.

Therefore, to get a scientific understanding of the dual attribute and its interaction effects on the data factor, it is necessary to take the following steps. We should promote institutional design that amplifies the positive externality, with a focus on facilitating public data sharing and improving the value of commercial data development. Also, we need to strengthen institutional arrangements that prevent and control the negative externality by emphasizing data supervision based on data types and levels as well as the rule of law.

This study aims to uncover and develop explicit knowledge of existing smart communities (SCs) to guide services and business solutions for enterprises and serve community users in a well-thought-out manner. These sagacious frameworks will assist in analyzing trends and reaching out to pre-existing setups with different degrees of expertise.

A systematic overview is provided in this paper to unify insights and competencies toward building SCs; a hybrid analytical approach is used consisting of machine learning and bibliometric analysis. Scopus and Web of Science (WoS) are the primary databases for this purpose.

SCs implement cutting-edge technologies to enhance mobility, elevating information and communication technology (ICT) skills and data awareness while improving business processes and efficiency. This system of SC is an evolution of the conventional method. It provides a foundation for intelligent community services based on individual users and technologies such as the Internet of Things (IoT), artificial intelligence, cloud computing and big data. Manufacturing-based, service-based, retail-based, resource management and infrastructure-based SCs exist in the literature.

The paper summarizes a conceptual framework of SCs based on existing works around SCs. To the best of the authors’ knowledge, this is the first systematic literature review that uses a hybrid approach of topic modeling and bibliometric analysis to understand SCs better.