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The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

The rapid, yet low-profit, expansion of the production capacity of state-owned enterprises (SOEs) represents a remarkable phenomenon. However, the motivation behind this key operational decision remains underexplored, especially concerning the prioritization of sociopolitical and financial goals in operations management. Drawing on the multiple-goal model in the behavioral theory of the firm (BTOF), the authors' study aims to examine how SOE capacity expansion is driven by performance feedback regarding the sociopolitical goal of employment provision and how SOEs differently prioritize sociopolitical and financial goals based on negative versus positive feedback on the sociopolitical goal.

The authors' study uses panel data on 826 Chinese SOEs in manufacturing industries from 2011 to 2019. The authors employ the fixed-effects model with Driscoll–Kraay standard errors, which are robust to heteroscedasticity, autocorrelation and cross-sectional dependence.

The authors find that SOEs increase capacity expansion as sociopolitical feedback becomes more negative, but they may not increase capacity expansion in response to positive sociopolitical feedback. Moreover, negative profitability feedback strengthens SOEs' capacity expansion in response to negative sociopolitical feedback. In contrast, negative profitability feedback weakens their response to positive sociopolitical feedback.

The authors' study offers a novel behavioral explanation of SOEs' operational decisions regarding capacity expansion. While the literature has traditionally assumed multiple goals as either hierarchical or compatible, the authors extend the BTOF's multiple-goal model to illuminate when firms pursue sociopolitical and financial goals as compatible (i.e. the activation rule) versus hierarchical (i.e. the sequential rule), thereby reconciling their tension in distinct performance situations. Practically, the authors provide fine-grained insights into how operations managers can prioritize multiple goals when making operational decisions. The authors' study also shows how policymakers can influence SOE operations to pursue sociopolitical goals for public benefit.

This study aims to investigate dynamic relations among office property price indices of 10 major cities in China for the years 2005–2021.

Using monthly data, the authors adopt vector error correction modeling and the directed acyclic graph for the characterization of contemporaneous causality among the 10 indices.

The PC algorithm identifies the causal pattern, and the linear non-Gaussian acyclic model algorithm further determines the causal path from which we perform innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tier of cities.

This suggests that policies on office property prices, in the long run, might need to be planned with particular attention paid to the top tier of cities.

The study aims to investigate the firm reaction to external pressures and the subsequent transformation to green innovation (GI) and sustainability, leading to an enhanced circular supply chain performance. The research examined the effect of coercive, normative and mimetic pressures on GI within the Indian tire industry context.

The authors have designed a self-administered questionnaire and received 283 responses. Nonresponse bias analysis was conducted first before proceeding to other assumptions such as constant variance and normality. The confirmatory factor analysis was used to check the data’s integrity and reliability. The PLS-structural equation modeling (SEM) is used to validate the constructs and test the established hypotheses.

External regulatory pressures significantly impact the tire industry’s move towards GI. Normative pressures from societal expectations and industry standards, along with mimetic pressures where companies emulate best practices, both drive the industry toward sustainable initiatives. Further, a clear positive relationship emerges between GI and sustainable supply chain practices (SSCP). This shift emphasizes recycling, reusing and reducing waste and showcases the industry’s commitment to minimizing its environmental impact.

Unlike existing literature that predominantly explores SSCP, the study introduces an integrated framework that intertwines institutional pressures (IP), green innovation (GI), and SSCP to elevate circular supply chain practices (CSCP). The current study highlights the relationships and practical implications within the Indian tire industry.

A substantial amount of research has examined the firm value impact of corporate social responsibility (CSR). Nevertheless, the findings have been inconsistent, prompting researchers to identify contingencies under which the impact varies. This study examines how information technology (IT)-enabled knowledge capabilities moderate the relationship between CSR and firm value.

We conducted the ordinary least squares (OLS) regression analysis on a sample of S&P 500 companies spanning from 2010 to 2017. We employed additional methods to test the robustness of the results, including the generalized method of moments (GMM) estimator and the two-stage least squares (2SLS) method.

The results show that IT-enabled absorptive capability (IT-AC) and IT-enabled social integration capability (IT-SIC) positively moderate the CSR–value relationship. Further, their moderating effects vary in distinct ways when environmental dynamism changes, hinting at the distinct underlying rationales behind the moderating roles of IT-AC and IT-SIC.

This study improves the understanding of the business value of CSR and IT. It has limitations in generalizability due to the use of secondary data.

This study provides practical guidelines to managers about how to strategically leverage IT resources for the creation of CSR value.

Encouraging businesses to enhance their CSR efforts and uphold sustainability extends beyond our immediate benefit and impacts future generations as well. However, due to an imbalance between costs and returns, companies often refrain from being wholeheartedly devoted to CSR. Our insights on guiding companies to derive more value from CSR can inspire their greater investment in CSR. Meanwhile, companies can obtain additional returns from deployed IT.

This study extends the IT business value literature by revealing how IT generates firm value in the context of CSR. It also adds critical insights into the mixed findings in previous research regarding the CSR–firm value link. The study’s findings offer useful guidance on the strategic deployment and utilization of IT resources to facilitate the creation of CSR value.

A wealth of studies have identified numerous antecedents to online self-disclosure. However, the number of competing theoretical perspectives and inconsistent findings have hampered efforts to obtain a clear understanding of what truly influences online self-disclosure. To address this gap, this study draws on the antecedent-privacy concern-outcome (APCO) framework in a one-stage meta-analytical structural equation modeling (one-stage MASEM) study to test a nomological online self-disclosure model that assesses the factors affecting online self-disclosure.

Using the one-stage MASEM technique, this study conducts a meta-analysis of online self-disclosure literature that comprises 130 independent samples extracted from 110 articles reported by 53,024 individuals.

The results reveal that trust, privacy concern, privacy risk and privacy benefit are the important antecedents of online self-disclosure. Privacy concern can be influenced by general privacy concern, privacy experience and privacy control. Furthermore, moderator analysis indicates that technology type has moderating effects on the links between online self-disclosure and some of its drivers.

First, with the guidance of the APCO framework, this study provides a comprehensive framework that connects the most relevant antecedents underlying online self-disclosure using one-stage MASEM. Second, this study identifies the contextual factors that influence the effectiveness of the antecedents of online self-disclosure.

The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems. However, the existing algorithms rely mostly on common ratings of items and do not consider temporal information about items or user interests. To solve this problem, this study proposes a new user-item composite filtering (UICF) recommendation framework by leveraging temporal semantics.

The UICF framework fully utilizes the time information of item ratings for measuring the similarity of items and takes into account the short-term and long-term interest decay for computing users’ latest interest degrees. For an item to be probably recommended to a user, the interest degrees of the user on all the historically rated items are weighted by their similarities with the item to be recommended and then added up to predict the recommendation degree.

Comprehensive experiments on the MovieLens and KuaiRec datasets for user movie recommendation were conducted to evaluate the performance of the proposed UICF framework. Experimental results show that the UICF outperformed three well-known recommendation algorithms Item-Based Collaborative Filtering (IBCF), User-Based Collaborative Filtering (UBCF) and User-Popularity Composite Filtering (UPCF) in the root mean square error (RMSE), mean absolute error (MAE) and F1 metrics, especially yielding an average decrease of 11.9% in MAE.

A UICF recommendation framework is proposed that combines a time-aware item similarity model and a time-wise user interest degree model. It overcomes the limitations of common rating items and utilizes temporal information in item ratings and user interests effectively, resulting in more accurate and personalized recommendations.

Impoverished employee mental health is harmful to employees and organisational performance. There is emerging interest in employee mental health in the human resource management (HRM) field. The majority of these studies mainly focus on the organisational and individual levels from the psychological and managerial perspectives without considering the sectoral characteristics and societal context.

This perspective paper draws on extant literature as well as 10 informal interviews with medical professionals, organisational leaders in the public sector, teachers and HR professionals to shed light on employee mental health research, practice and challenges in the Chinese context.

This paper reveals national, sectoral, occupational and individual factors that shape mental health problems, individual coping mechanisms and organisational interventions. It also shows international influence on employee mental health in the form of institutional pressure and knowledge transfer.

There is limited research on employee mental health and HRM in the Chinese context, which restricts the scope of discussion in this paper, but at the same time presents rich future research opportunities that may be relevant to other national settings.

Managing the mental health of the workforce is part of the mental health management of the population, which means a holistic approach to building a mental health eco-system needs to be adopted. The authors call for more research on employee mental health in the Chinese context to provide evidence to support policy development and organisational efforts to scale up mental health services at the national and organisational levels. The authors also provide practical recommendations for policymakers and employing organisations.

The authors present a multi-level and multi-factor overview related to employee mental health in the Chinese context. The authors argue for a resource-based and multi-stakeholder approach, which will help inform and improve mental health policy and practice. The authors present several avenues for future scholarship and research. The authors extend the research frontiers of employee mental health issues by calling for the inclusion of a broader range of theoretical lenses including institutional theory, cultural and spiritual perspective and critical sociology to understand more fully how employee mental health conditions may be undermined or improved.

This study aims to explore a new restaurant category’s development from birth to maturity, thereby explaining the rationale for category innovation strategies.

The authors conducted a qualitative case study analysis of the New Chinese-style Fusion Restaurant category’s development from birth to maturity. Thematic analysis was conducted on data collected from semi-structured interviews and textual information.

A new restaurant category’s maturation is determined by the formation of society’s shared knowledge about the category’s crucial attributes, which is an outcome of market participants’ category-related social practices. The authors develop a novel, four-stage framework for the socialized construction of this shared knowledge: a knowledge creation (KC), knowledge diffusion (KD), knowledge integration (KI) and knowledge structuralization (KS). This knowledge evolution along this KC–KD–KI–KS sequence can holistically describe the category maturation process. This framework can help understand the rationale for a restaurant category’s maturation by analyzing the interrelationships among market participants’ social practices, knowledge-related activities and market development.

This study explains how market participants’ knowledge-related activities facilitate a new restaurant category’s maturation. This can help restaurant managers cope with increasingly homogeneous competition by applying a category-innovation strategy.

This study extends product categorization research on restaurants by articulating a product category’s maturation process from a knowledge perspective.

The purpose of this study is to investigate the impact of the accounting system reform on corporate innovation behavior and the heterogeneity and underlying mechanisms of this impact. This paper further aims to study the impact of accounting system reform on corporate value.

This study takes China's A-share listed corporates as a sample and uses the exogenous policy shock of the implementation of the New Accounting Standards in 2007 to design the identification strategy of propensity score matching and difference-in-differences method. By comparing the differences between the innovation level of corporates in high-tech industries and non-high-tech industries before and after the implementation of the New Accounting Standards, the impact of the accounting system reform on corporates' innovative behavior can be identified.

Results show that compared with corporates in traditional industries, high-tech corporates obtained higher patent output after the implementation of the New Accounting Standards. This reform mainly affects corporate innovation by improving corporate risk-taking. In addition, this paper finds that the reform of the accounting system has increased the market value of high-tech corporates in the long run.

This study provides new empirical evidence for addressing the insufficient innovation incentives for market entities and enriches the existing literature on the economic effects of the change of accounting systems and the influencing factors of corporate innovative behavior from the accounting system perspective.