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This study aims to investigate the “digitalization paradox” in manufacturing digital transformation, where significant investments in digital technology may not necessarily lead to increased returns. Specifically, it explores the intricate relationship between digital technology convergence, financial performance, productivity and technological innovation in listed Chinese manufacturing firms, drawing upon theories of digital innovation and knowledge networks.

Using a large panel data from 747 listed firms in China’s manufacturing sector and their 428,927 patents spanning from 2013 to 2022, this research first quantifies manufacturing firm-level digital technology convergence through patent network analysis. Furthermore, this study employs hierarchical regression analysis and the instrumental variable method to investigate the curvilinear relationship between digital technology convergence and financial performance. Furthermore, the moderating role of firms’ productivity and technological innovation is tested.

Three types of firm-level digital technology convergence (DTC) are delineated and quantified: local authority in digital convergence (DegreeDTC), convergence with heterogeneous digital knowledge (BetweenessDTC) and shortest-path convergence with digital technologies (ClosenessDTC, where a higher value signifies a more conservative and shorter path in adopting digital technologies). Network visualization shows that manufacturing firms' DTC has consistently increased over time. Contrary to traditional assumptions, our research reveals a U-shaped relationship between DTC (specifically, DegreeDTC and BetweenessDTC) and financial performance. This relationship is characterized by a negative correlation at lower levels and a positive one at higher levels. The joint effect of firms’ productivity and technological innovation significantly strengthens this relationship. These findings are robust across a series of robustness checks.

Our findings offer practical insights for both managers and policymakers. We recommend a balanced approach to digital innovation management within the technology convergence paradigm. Manufacturing firms can generate economic value by strategically choosing to either shrink or expand their digital technology application areas, thereby reducing uncertainties related to emerging convergent businesses. Additionally, the study underscores the synergistic strategy of combining innovation with productivity. Within the DTC business context, integrating productivity with technological innovation not only enhances cost flexibility but also improves problem-solution matching, ultimately amplifying synergistic benefits.

To the best of our knowledge, this is the first study to apply a digital technology co-occurrence network to unveil nuanced relationships in “DTC – finance performance” within the manufacturing sector. It challenges conventional thinking regarding the common positive effect of digital innovation and technological convergence. This study provides a comprehensive analysis of DTC, financial performance, productivity and technological innovation dynamics, as well as offers managerial implications for managers and policymakers.

• (1)

  We quantify manufacturing firm-level DTC through patent network analysis and find consistent increases over time.

• (2)

  A significant U-shaped relationship between DTC and financial performance, being negative at lower levels and positive at higher levels.

• (3)

  The joint effect of firms’ productivity and technological innovation reinforces this relationship by distributing costs and enhancing synergistic benefits.

• (4)

  We challenge existing literature by uncovering a complex relationship in “DTC – finance performance”, contrary to popular belief of a monotonic effect of digital innovation or technological convergence.

We quantify manufacturing firm-level DTC through patent network analysis and find consistent increases over time.

A significant U-shaped relationship between DTC and financial performance, being negative at lower levels and positive at higher levels.

The joint effect of firms’ productivity and technological innovation reinforces this relationship by distributing costs and enhancing synergistic benefits.

We challenge existing literature by uncovering a complex relationship in “DTC – finance performance”, contrary to popular belief of a monotonic effect of digital innovation or technological convergence.

The objective of this paper is to propose a consumer-behavior-based intelligence (CBBI) model to identify market structure so as to monitor product competition. Competitive intelligence extracted from Chinese e-business clickstream data is exploited to examine the relevance of consumers' heterogeneous behavioral feedback, namely, click, tag-into-favorite, time-of-browsing, add-into-cart, and remove-from-cart, to visualize the competitive product market structure and to predict product-level sales.

Our proposed CBBI model consists of visualization and prediction, which explore e-business clickstream data. We conduct the visualization and segmentation of market structure in the form of a perceptual map by employing K-means clustering algorithm and multidimensional scaling technique. Concurrently, we developed an updated Bayesian linear regression (BLR) to predict product-level sales by considering consumers' heterogeneous feedback. Our updated BLR specifically integrated the estimated knowledge of the previous periods to verify whether product sales are period-dependent due to the consumer memory effect in e-commerce, improving the conventional BLR of diffuse prior distribution setup in terms of mean absolute error (MAE) and root mean squared error (RMSE).

Considering the performance of consumers' heterogeneous actions, the present research visualized three different segments of the competitive market structure in a perceptual map, and its horizontal axis is shown as a signal of the ascending trend of product sales. The previous five-day period was ascertained to be the best size of a time window for the consumer memory effect on product sales prediction. This hypothesis is supported by the concept that product sales are period-dependent. The results of the proposed updated BLR indicate that consumer tag-into-favorite, add-into-cart, and remove-from-cart feedback have positive impacts on product-level sales while click and time-of-browsing have the opposite effect.

While the identified competitive product market structure elaborates consumer heterogeneous feedback toward alternative product choices, this paper contributes by extending those homogeneous consumer preferences-related marketing studies. The perceptual map's configuration in respect to period-dependent product sales facilitates the effective inclusion of consumer behavior application in product sales prediction research in e-commerce. This paper helps sellers and retailers better comprehend the impacts of heterogeneous feedback and the consumer memory effect on the degree of competition in the form of product sales. The research results also offer a managerial implication about shaping the competitive edge by conducting different product management strategies in e-commerce platforms.

The purpose of this paper is to identify potential competitive relationships among brands by analyzing the dynamic clicking behavior of consumers.

Consumer sequential online click data, collected from JD.com, is used to analyze the dynamic competitive relationship between brands. It is found that the competition intensity across categories of products can differ considerably. Consumers exhibit big differences in purchasing time of durable-like goods, that is, the purchasing probability of such products changes considerably over time. The local polynomial regression model (LPRM) is used to analyze the relationship between brand competition of durable-like goods and the purchasing probability of a particular brand.

The statistical results of collective behaviors show that there is a 90/10 rule for the category durable-like goods, implying that ten percent of the brands account for 90 percent market share in terms of both clicking and purchasing behavior. The dynamic brand cognitive process of impulsive consumers displays an inverted V shape, while cautious consumers display a double V shaped cognitive process. The dynamic consumers’ cognition illustrates that when the brands capture a half of the click volume, the brands’ competitiveness reaches to its peak and makes no significant different from brands accounting for 100 percent of the click volume in terms of the purchasing probability.

There are some limitations to the research, including the limitations imposed by the data set. One of the most serious problems in the data set is that the collected click-stream is desensitized severely, restricting the richness of the conclusions of this study. Second, the data set consists of many other consumer behavioral data, but only the consumer’s clicking behavior is analyzed in this study. Therefore, in future research, the parameters brand browsing by consumers and the time of browsing in each brand should be added as indicators of brand competitive intensity.

The authors study brand competitiveness by analyzing the relationship between the click rate and the purchase likelihood of individual brands for durable-like products. When the brand competitiveness is less than 50 percent, consumers tend to seek a variety of new brands, and their purchase likelihood is positively correlated with the brand competitiveness. Once consumers learn about a particular brand excessively among all other brands at a period of time, the purchase likelihood of its products decreases due to the thinner consumer’s short-term loyalty the brand. Till the brand competitiveness runs up to 100 percent, consumers are most likely to purchase a brand and its product. That indicates brand competitiveness maintain 50 percent of the whole market is most efficient to be profitable, and the performance of costing more to improve the brand competitiveness might make no difference.

There are many studies on brand competition, but most of these research works analyze the brand’s marketing strategy from the perspective of the company. The limitation of this research is that the data are historical and failure to reflect time-variant competition. Some researchers have studied brand competition through consumer behavior, but the shortcoming of these studies is that it does not consider sequentiality of consumer behavior as this study does. Therefore, this study contributes to the literature by using consumers’ sequential clicking behavior and expands the perspective of brand competition research from the angle of consumers. Simultaneously, this paper uses the LPRM to analyze the relationship between consumer clicking behavior and brand competition for the first time, and expands the methodology accordingly.

With a fast-growing Muslim population and consumer income, the demand for halal products by Chinese Muslims has expanded strongly. However, literature addressing Chinese Muslims’ consumption is limited, and their demand for halal products is little understood. This study aims to investigate what affects Chinese Muslims’ demand for halal products, with a focus on halal personal care products.

A survey of 500 respondents was conducted to collect cross-sectional data in northwest China. Data were processed and analysed with a logit model.

Apart from faithfulness, reliability of recommendations, product price, product availability and halal authenticity are most important determinants influencing the purchase of halal products by Chinese Muslims.

In this study, the focus is only on Muslims from China’s Northwest. Due to various constraints, the cluster and convenience sampling methods are used.

The findings are invaluable for governments and industry bodies to form policies to better meet the burgeoning demand for halal products by Chinese Muslims. They are also very invaluable for producers and exporters who intend to penetrate the halal market in non-Muslim-dominant countries like China.

Studies on understanding the needs of Muslims in non-Muslim countries are limited. Given the sheer size of the Muslim population in China, understanding their demand for halal products and influential determinants concerning such demand adds to the literature and helps the industry to better serve and capitalise on the growing market.

With the development of economy and the progress of science and technology, the function of Luoyang city is constantly enriched and expanded. The city is no longer a simple area for people to live in. The function structure of the city is affected by the external factors and is constantly expanding. Urban logistics is one of the important influencing factors. To more comprehensively and accurately understand the status and role of logistics network space in urban planning, the characteristics and development law of urban logistics structure from the theoretical point of view are analyzed in this paper with Luoyang City as the research background. The city logistics function is determined as one of the main factors affecting the economic growth of the city is determined through the literature search method, consulting professionals and other research methods. Urban road planning and design are the focus of the study; the planning and development of urban logistics road network in Luoyang City are compared and analyzed. In full consideration of the necessity of the optimization of the urban logistics network space, the preliminary optimization scheme design and suggestion of Luoyang logistics is introduced. At the end of this paper, the sustainable development and the status of the future urban logistics function are analyzed and prospected. Continuous research and analysis of multiple subjects and angles are still needed.

Since most literature on international performance appraisal is derived from Western MNCs, it is questionable whether the Western theories are equally powerful when applied to other national contexts. This study develops the Chinese international performance appraisal model by exploring performance appraisal policies and practices and the associated factors in Chinese MNCs. It reveals that Chinese MNCs adopt different approaches towards different groups, particularly different nationalities and managerial status. The Chinese international performance appraisals are a mix of home and local appraisal systems, and a mix of traditional Chinese personnel management and modern Western HRM concepts. Moreover, Chinese international performance appraisal policies and practices are affected by various host‐contextual and firm‐specific factors, and there is also an interplay between international performance and other international human resource management activities.

The purpose of this paper is to examine the drivers, practices, and outcomes of social media use in the management of organizational supply chain.

Online questionnaire survey was used to collect data from 285 organizations representing different industries in China. The data then were analyzed with structure equation modeling using SmartPLS.

The results indicate that key antecedents such as external pressures, internal readiness, expected benefits, strategic goals, and perceived risks influence organizational social media use, which subsequently impact organizational performance outcomes in operation and marketing as well as the satisfaction level of both internal and external constituents, such as customers, employees, partners, and suppliers.

The study obtained data about one organization from only one respondent and did not used random sampling.

This study provides insights on why and how companies should use social media for relationship building and business outcomes.

Drawing from the resources-based view, social networks, strategic choice theory, and technology organization and environment framework, a new social media utilization model for business outcome was established and testified using empirical data. This study is one of the first studies that adopts technology-organization-environment (TOE) framework of technology adoption theory to study organizational social media use. The findings in this study confirm the validity of the TOE framework for analyzing social media adoption and use in various organizations.

Foreign direct investment in the real estate (FDIRE) sector is required to bridge the gap between investment needed and domestic funds. Further, foreign direct investment is gaining importance because other sources of raising finance such as External Commercial Borrowing and foreign currency convertible bonds have been banned in the Indian real estate sector. Therefore, the objective of the study is to explore the determinants attracting foreign direct investment in real estate and to assess the impact of those variables on foreign direct investments in real estate.

Johansen cointegration test, vector error correction model along with variance decomposition and impulse response function are employed to understand the nexus of the relationship between various macroeconomic variables and foreign direct investment in real estate.

The results indicate that infrastructure, GDP and tourism act as drivers of foreign direct investment in real estate. However, interest rates act as a barrier.

This article aimed at exploring factors attracting FDIRE along with estimating the impact of identified variables on FDI in real estate. Unlike other studies, this study considers FDI in real estate instead of foreign real estate investments.