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Innovation through tradition (ITT) is an increasingly important area of research particularly in the creative and cultural industries. The purpose of this study is to develop a process framework of ITT for rural heritage bed and breakfast (B&B) sector and investigate the antecedents and challenges of ITT implementation.

Nine heritage B&Bs displaying successful ITT in Songyang county in China were selected as the research site. Multiple cases of B&B were interviewed, and some observations were conducted. This study adopted the process-oriented reflexive critical incident technique to collect qualitative data and analysed it thematically.

Based on the findings, a five-phase innovation framework is proposed to demonstrate how ITT could be achieved in practice. These phases are idea generation, idea evaluation, initial implementation, continuing implementation and sustaining improvement. Three key antecedents (experience corridors, networks and institutional pressures) of ITT implementation were also identified.

This study has showed that rural heritage B&Bs can differentiate themselves from competition by the means of ITT. This study proposes a process framework for this kind of innovation bringing to light the required steps, the antecedents and key activities which the practitioners should pay great attention. This study highlights the needs for continuing and sustaining innovations in the long term.

This study proposes a novel five-phase process framework of ITT to encompass the innovation activities in heritage setting. For practitioners, this study recommends enhancing the sensing capability of local entrepreneurs through personal travel experience and establishing business networks as the key antecedents of a successful ITT under rural heritage setting.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

In the past two decades, the way enterprises in the People’s Republic of China (PRC) manage their human resources has changed dramatically. Multinational enterprises (MNEs) have entered into the “strategic investor” phase, where now the integration of PRC operations into the MNE network receives growing attention. For these companies HRM is often of high strategic importance. This article seeks to explore how differences in HRM practices in such businesses vary with their ownership forms. The study is based on in‐depth interviews with HRM managers – on the PRC country‐level – in 12 MNEs. The large majority of the participating companies clearly stated that today the HRM function is of high strategic importance for their operations in the PRC and is predominantly controlled by the MNE partner. Although equity ownership stake is an important variable influencing HRM policies and practices, it is shown that it needs to be seen in conjunction with other possibly non‐equity control‐mechanisms.

In shield tunneling projects, human, shield machine and underground environment are tightly coupled and interacted. Accidents often occur under dysfunctional interactions among them. Therefore, this paper aims to develop a multi-agent based safety computational experiment system (SCES) and use it to identify the main influential factors of various aspects of human, shield machine and underground environment.

The methods mainly comprised computational experiments and multi-agent technologies. First, a safety model with human-machine-environment interaction consideration is developed through the multi-agent technologies. On this basis, SCES is implemented. Then computational experiments are designed and performed on SCES for analyzing safety performance and identifying the main influential factors.

The main influential factors of two common accidents are identified. For surface settlement, the main influential factors are ranked as experience, soil density, soil cohesion, screw conveyor speed and thrust force in descending order of influence levels; for mud cake on cutter, they are ranked as soil cohesion, experience, cutter speed and screw conveyor speed. These results are consistent with intuition and previous studies and demonstrate the applicability of SCES.

The proposed SCES provides comprehensive risk factor identification for shield tunneling projects and also insights to support informed decisions for safety management.

A safety model with human-machine-environment interaction consideration is developed and computational experiments are used to analyze the safety performance. The novel method and model could contribute to system-based safety research and promote systematic understanding of the safety performance of shield tunneling projects.

(1) The shear lag effect and its additional deflection contribution to composite beams based on spatial grid elements were presented. (2) A refined spatial grid element analysis method that can simultaneously obtain the internal forces, displacements and stresses of various parts of a composite beam.

A refined spatial grid element analysis method.

The proposed method can directly obtain the internal forces and displacements of the joints of the composite beam roof, floor and web.

To comprehensively comprehend the mechanical behavior of double-girder steel plate composite girder bridge structures and facilitate refined analysis, this paper introduces a refined spatial grid element analysis model applicable to both the global and local domains.

This study aims to study the gas film stiffness of the spiral groove dry gas seal.

The present study represents the first attempt to calculate gas film stiffness in consideration of the slipping effect by using the new test technology for dry gas seals. First, a theoretical model of modified generalized Reynolds equation is derived with slipping effect of a micro gap for spiral groove gas seal. Second, the test technology examines micro-scale gas film vibration and stationary ring vibration to determine gas film stiffness by establishing a dynamic test system.

An optimum value of the spiral angle and groove depth for improved gas film stiffness is clearly seen: the spiral angle is 1.34 rad (76.8º) and the groove depth is 1 × 10^–5 m. Moreover, it can be observed that optimal structural parameters can obtain higher gas film stiffness in the experiment. The average error between experiment and theory is less than 20%.

The present study represents the first attempt to calculate gas film stiffness in consideration of the slipping effect by using the new test technology for dry gas seals.

The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.

The purpose of this paper is to examine empirically whether corporate governance mechanisms have an effect on income‐smoothing behavior in the People's Republic of China.

The sample comprises 1,358 companies listed in the Shanghai Stock Exchange and the Shenzhen Stock Market during the period 1999 to 2006. By comparing the variability of income to the variability of sales, an income smoother can be identified if income is less variable than sales.

The authors' empirical results show that income smoothing is more severe when the state is the controlling shareholder of the Chinese listed firm. Firms with more independent directors are more likely to engage in income smoothing. The governance mechanisms such as board of directors, supervisory board, audit committee, external auditors, and shareholders' participation are not effective in curtailing income smoothing in China.

For Chinese firms and especially government‐linked enterprises, the way in which they present themselves may be significant, since the image they present to potential strategic partners may be marred by suspicions of income smoothing.

The paper presents the current development of China's corporate governance system and indicates that agency conflicts between controlling shareholders and minority investors account for a significant portion of earnings management in China.

Past research often relies on linear relationship assumptions from the perspective of managers when studying the relationship between attribute performance and satisfaction. However, this study extracts tourists’ online reviews to explore asymmetric relationships and identifies island tourism satisfiers, hybrids and dissatisfiers.

The research uses 3,523 reviews from Tripadvisor to examine Langkawi Island’s tourist satisfaction. Latent Dirichlet allocation (LDA) machine-learning approach, penalty–reward contrast analysis and asymmetric impact-performance analysis (AIPA) were employed to extract and analyse the data.

Langkawi’s dissatisfiers included “hotel and restaurant”, “beach leisure”, “water sport”, “snorkelling”, “commanding view”, “waterfall”, “sky bridge walk”, “animal show”, “animal feeding”, “history culture”, “village activity” and “duty-free mall”. Amongst these, five were low performers. Hybrids encompassed “ticket purchasing”, “amenity” “traditional food market” and “gift and souvenir”, all of which were low performers. Only one attribute was categorised as a satisfier: “nature view” which performed exceptionally well.

This study provides recommendations to enhance tourist satisfaction and address tourist dissatisfaction. The elements requiring immediate attention for enhancement are the five low-performance dissatisfiers, as they represent tourists’ fundamental expectations. Conversely, the satisfier or excitement factor (i.e. nature views – mangroves and wildlife) could be prominently featured in promotional materials.

This research constitutes an early endeavour to categorise attributes of island tourism into groups of satisfaction, hybrid or dissatisfaction based on user-generated data. It is underpinned by two-factor and three-factor theories.