Search results

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

The purpose of this paper is to have a deeper understanding of the nonlinear relationship between the impact of climate change on tourism development. Current studies on the effects of climate change on tourism development primarily rely on linear correlation assumptions.

Based on the New Institutional Economics theory, the institutional setting inherently motivates and ensures the growth of the tourism industry. For a precise evaluation of the nonlinear consequences of climate change on tourism, this paper concentrates on Chinese cities between 2011 and 2021, methodically analyzing the influence of climate change on tourism.

The study findings suggest that there is an “inverse U”-shaped nonlinear relationship between climate change and tourism development, initially strengthening and subsequently weakening. Based on these findings, the research further delves into how institutional contexts shape the nonlinear association between climate change and tourism growth. It was found that in a higher institutional backdrop, the “inverse U” curve tends to flatten and surpass the curve adjusted for a lesser institutional context. Upon deeper mechanism analysis, it was observed that cities with more advanced marketization, improved industrial restructuring and enhanced educational growth exhibit a more evident “inverse U”-shaped nonlinear connection between climate change and tourism evolution.

First, previous studies on climate change and tourism development largely rely on questionnaire data (Hu et al., 2022). In contrast to these studies, this paper uses dynamic panel data, which to some extent overcomes the subjectivity and difficulty of causality identification in questionnaire data, making our research conclusions more accurate and reliable. Second, this study breaks through the linear relationship hypothesis of previous literature regarding climate change and tourism development. By evaluating the nonlinear relationship of climate change to tourism development from the institutional pressure perspective, it more intricately delineates their interplay mechanism, expanding and supplementing the research literature on the relationship mechanism between climate change and tourism development. Thirdly, the conclusions of this study are beneficial for policymakers to better understand and assess the scope of climate change impacts. It also aids relevant departments in clarifying the direction of institutional environment optimization to elevate the level of tourism development when faced with adverse impacts brought about by climate change.

Electric Vehicles (EVs), owing to their low carbon emissions, have gained immense importance in achieving net-zero emissions by 2070. They have also appeared as viable substitute to conventional vehicles. Aligning with global initiatives, India has created a favourable ecosystem and has implemented several policies since 2011 to achieve its target. Consequently, the market share of EVs has surged, both globally and in India, over the past decade. Taking this into account, this study aims to identify the factors that influence EVs in a developing economy using the context of India.

This study identified important determinants of EV adoption from global literature and employed a multiple linear regression model (MLRM) using the ordinary least squares (OLS) technique. Secondary data were utilised to identify determinants in the Indian context, sourced from the Ministry, NITI Aayog, AQI, the Lok Sabha Question, and the Economic Survey of India.

This study found that the number of charging stations and local pollution levels significantly influence EV adoption in India. The insignificance of the other variables may be due to the emerging state of the Indian EV market.

This study adds to the growing body of literature on EV adoption in developing economies by analysing the factors that impact its adoption using regional data. In addition, this study provides a unique perspective on a developing economy and advocates a comprehensive policy for EV adoption that reflects long-term sustainability.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2023-0479.

As buyers and suppliers seek to create value, they face the challenge of creating an environment that promotes coordination and information sharing and discourages opportunism. While the literature suggested dyadic mechanisms to create such an environment, this study focuses on ties beyond the buyer–supplier dyad. Specifically, close connections to one's partner's partners (CPP) are crucial in the realization of benefits for buyers and suppliers.

Drawing from embeddedness theory and governance theory, the authors developed a contingency framework to examine when CPP are beneficial or counterproductive considering two dyadic attributes – relational capital (RC) and partner dependence. Analyses were conducted using data from a dyadic survey complemented with archival data on 106 buyer–supplier relationships (BSRs).

The study reveals that CPP both help and hurt in the realization of benefits. Stark asymmetries exist between the impact of CPP on the buyer and supplier sides. For buyers, CPP exert a direct positive effect on operational and innovation benefits. For suppliers, the effect of CPP on operational and innovation benefits is contingent on buyer dependence and RC – CPP serves as a substitute for buyer dependence and RC. There are no such contingency effects for buyers. Further analysis identifies situations for suppliers when CPP hurt the realization of benefits.

The study highlights the importance of CPP to foster efficiency and innovation within BSRs and illustrates how their impact varies across contingency conditions and across the parties within a dyad.

Established in 2010, Mellower Coffee has 40 exquisite chain stores and three branches, namely Mellower Coffee Sales, Mellower Business Management and Shanghai Mellower Roasting Factory. Positioned as a premium coffee brand in China, Mellower Coffee has realized the integrated operation and management of the whole industrial chain from raw coffee trade, roasting factory, coffee retail products, specialty coffee chain, office coffee to coffee academy. It has a vision to attract and cultivate more and more coffee lovers by constant innovation coffee culture promotion.

The evolution of modern digitalization technologies necessitates the development of a competitive digital supply chain quality management (SCQM) strategy by manufacturers. Using the new institutions and institutional theory (IIT), the study research first aims to identify the most important SCQM practices that can influence competitive performance (CP). Second, the authors intend to investigate the role of digital strategy alignment (DSA) in moderating the relationship between the multidimensionality of SCQM practices and CP among manufacturers.

The authors employ the Partial Least Squares-Structural Equation Modeling (PLS-SEM) technique to examine 225 valid samples from Malaysian manufacturers who use SCQM practices.

The study findings indicate that five of the twelve hypotheses developed were accepted. This suggests that supplier focus, strategic collaboration, information sharing and customer focus are positively and significantly correlated with CP. Unexpectedly, DSA moderates the relationship between leadership and CP.

This study extended the new IIT by empirically testing the six SCQM practices for CP in a DSA context, which can serve as a model for future research in the SCQM, CP and DS fields.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This study aims to investigate the rising trend of virtual influencers – digitally created characters with human-like attributes. It aims to evaluate and compare their effectiveness with human influencers in terms of brand attitudes and purchase intentions. It uncovers the mechanisms underlying the differences in effectiveness observed between virtual influencers and their human counterparts.

The research uses a 2 (influencer type: human vs virtual) × 3 (product type: functional vs symbolic vs experiential) between-subjects design. Through a pilot study (n = 334) and a main study (n = 352), the research examines the interactive effects of influencer and product type on brand attitude and purchase intentions. Hypotheses were developed and tested using moderating mediation models centered on authenticity and product–endorser fit.

Virtual influencers are perceived as less authentic than human influencers across all product types, negatively influencing brand attitude and purchase intention. However, the extent to which influencer type affects brand attitudes and purchase intentions, mediated by product–endorser fit, varies based on the product type.

This research emphasizes two key mechanisms – authenticity and product–endorser fit – influencing the effectiveness of virtual influencers. It suggests that aligning virtual influencers with suitable product types can offset their perceived authenticity deficit, significantly affecting their endorsement effectiveness.

Few studies have conceptualized how companies can build and nurture international dynamic marketing capabilities (IDMCs) by implementing growth hacking strategies. This paper conceptualizes growth hacking, a managerial-born process to embed a data-driven mind-set in marketing decision-making that combines big-data analysis and continuous learning, allowing companies to adapt their dynamic capabilities to the ever-shifting international competitive arenas.

Given the scarcity of studies on growth hacking, this paper conceptualizes this managerial-born concept through the double theoretical lenses of IDMCs and information technology (IT) literature.

The authors put forward research propositions concerning the four phases of growth hacking and the related capabilities and routines developed by companies to deal with international markets. Additional novel propositions are also developed based on the three critical dimensions of growth hacking: big data analytics, digital marketing and coding and automation.

Lack of prior conceptualization as well as the scant literature makes this study liable to some limitations. However, the propositions developed should encourage researchers to develop both empirical and theoretical studies on this managerial-born concept.

This study develops a detailed compendium for managers who want to implement growth hacking within their companies but have failed to identify the necessary capabilities and resources.

The study presents a theoretical approach and develops a set of propositions on a novel phenomenon, observed mainly in managerial practice. Hence, this study could stimulate researchers to deepen the phenomenon and empirically validate the propositions.

This paper aims to revisit the relationship between sales growth and profitability by exploring the direct and indirect effects of cost stickiness in the growth process. Cost stickiness refers to asymmetric variations of costs associated with increases and decreases in sales. Cost stickiness is analyzed as a strategic liability that negatively affects profitability because it contributes to organizational rigidity that causes opportunity costs.

The empirical design is based on a large sample of 65,599 French firms drawn from the Amadeus database and it covers the period 2010 to 2019. The authors take advantage of the presentation of expenses made by nature in Amadeus to calculate cost stickiness in a more direct way than what is commonly done in the literature. The authors use various regression models to test the hypotheses.

For firms that experience rapid growth in sales, cost stickiness has a positive moderating effect on the relation between sales growth and profitability because of a higher asset turnover efficiency. However, for firms that experience slow growth, no growth or a decrease in sales, cost stickiness plays a negative moderating effect on the relation between sales and profitability.

This work contributes to the discussion about the conditions under which high growth is associated with greater profitability and conceptualizes cost stickiness as a strategic liability. The empirical context, privately held firms, has been overlooked by previous research.