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Small-to-medium-sized enterprises (SMEs) in e-commerce often invest in information technology (IT) to stay competitive. However, whether and how IT capability (ITC) translates into financial performance requires further research. This paper examines the role of ITC in enabling value proposition innovation (VPI) as an important mechanism that improves financial performance for Chinese e-commerce SMEs during the COVID-19 pandemic. We argue that ITC is critical for enabling innovation because it elevates SMEs’ understanding of changing customer needs, especially when SMEs operate on multiple e-commerce platforms (multihome).

We used partial least squares structural equation modeling (PLS-SEM) and tested the hypotheses that ITC mediated by VPI and moderated by multihoming increases the financial performance of e-commerce SMEs through a survey among 206 Chinese SMEs operating on Taobao.

We find that not only higher levels of ITC lead to better financial performance, but also that the effect is fully mediated by VPI. Moreover, the effect of ITC on innovation is enhanced when vendors operate on multiple platforms.

The study identifies VPI as an important mechanism through which SMEs can leverage their ITC to adapt, innovate and thrive in competition. Our work suggests that using technology to develop innovative ideas and identify opportunities (which are reflected in VPI) is key to success and that doing so is more likely when vendors multihome. Thus, this study contributes to the innovation literature by explicating a concrete link between ITC, multihoming, VPI and increased financial performance. Different e-commerce stakeholders, including SME owners, IT and service providers and e-commerce platforms, can benefit from the findings of this work.

The purpose of this paper in the editorial review of manuscripts to highlight the emergent aspects of original thinking that provide new perspectives on issues central to management.

A synoptic perspective of a very broad range of topics covered in research by scholars is provided: education, corporate value, quality, corporate social responsibility, risk behavior, managing reputation, expatriation, growth and creativity. In this particular review, the editor emphasizes those facets of the paper that shed original, new light on management. Also, the areas where research provides a theoretical basis for consulting or managerial practice are highlighted to illustrate how research can be of practical relevance.

Some of the insights gained clearly indicate the importance of research. For example, tighter or more rigorous governmental regulations on food quality are unlikely to contribute to the enhancement of firms’ capability in improving food quality. The government needs to do much more than only regulate. Then, there is empirical reaffirmation that guan-xi with government matters: better the relationships, the higher corporate value but only if the firm is in private hands, not for state-owned enterprises (SOEs). Then, the Chinese local firms’ nexus with foreign, and international corporations influences their corporate social performance. Even more fascinating, a bank’s risk behavior is dependent on the structural composition of the board of directors. Indeed, research shows that a gender mix in fact lowers the propensity toward taking a high risk. Then, through reflecting upon their deep researches, the authors derive a theoretic framework for coping with reputation loss following a financial restatement. Here, authors showed SOEs to suffer much more in the event of restatement than the privately owned enterprises. Then, in a piece of innovative research of expatriation, authors relate a spectrum of the Chinese cultural values to performance. In a cross-country (civilization) study, the research motivation is: Whether SMEs in China, India and Pakistan are confronting the same environmental challenges? As expected, the answer would be yes in some aspects and no in others. This a significant finding, as the Chinese are raised on a milieu of suppressing one’s emotions: emotional expressiveness can contribute to creativity!

In this paper, the editor highlights some aspects on the original thinking of the authors within this issue of Chinese Management Studies. Also, the editor highlights on how research contained herein is contributing to managerial (consulting) practice.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

Motivated by the problems that the positioning error of strap-down inertial navigation system (SINS) accumulates over time and few sensors are available for midwater navigation, this paper aims to propose a self-aided SINS scheme for the spiral-diving human-occupied vehicle (HOV) based on the characteristics of maneuvering pattern and SINS error propagation.

First, the navigation equations of SINS are simultaneously executed twice with the same inertial measurement unit (IMU) data as input to obtain two sets of SINS. Then, to deal with the horizontal velocity provided by one SINS, a delay-correction high-pass filter without phase shift and amplitude attenuation is designed. Finally, the horizontal velocity after processing is used to integrate with other SINS.

Simulation results indicate that the horizontal positioning error of the proposed scheme is less than 0.1 m when an HOV executes spiral diving to 7,000 meters under the sea and it is inherently able to estimate significant sensors biases.

The proposed scheme can provide a precise navigation solution without error growth for spiral-diving HOV on the condition that only IMU is required as a navigation sensor.

Deep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations.

Using bibliometric analysis and the SPAR-4-SLR protocol, this research conducts a systematic literature review to present a scientific mapping of top-tier research on DL applications in manufacturing operations.

This research discovers and delivers key insights on six knowledge clusters pertaining to DL applications in manufacturing operations: automated system modelling, intelligent fault diagnosis, forecasting, sustainable manufacturing, environmental management, and intelligent scheduling.

This research establishes the important roles of DL in manufacturing operations. However, these insights were derived from top-tier journals only. Therefore, this research does not discount the possibility of the availability of additional insights in alternative outlets, such as conference proceedings, where teasers into emerging and developing concepts may be published.

This research contributes seminal insights into DL applications in manufacturing operations. In this regard, this research is valuable to readers (academic scholars and industry practitioners) interested to gain an understanding of the important roles of DL in manufacturing operations as well as the future of its applications for Industry 4.0, such as Maintenance 4.0, Quality 4.0, Logistics 4.0, Manufacturing 4.0, Sustainability 4.0, and Supply Chain 4.0.

Mindful consumption is a popular concept that is often associated with mindfulness and religious faith, but nonetheless, its empirical associations to these remain relatively underexplored. Clarifying the impact of mindfulness and religious faith on mindful consumption is important to delineate their effectiveness in influencing consumers to reconsider consumption decisions (e.g. the need for additional products) given the detrimental effects of mindless consumption (e.g. financial debt, environmental degradation and materialistic culture). The concern about mindfulness potentially being a religious matter can also be resolved through empirical validation. Hence, the purpose of this research is to advance the empirical understanding of how mindfulness and religious faith impact on mindful consumption and whether mindfulness and religious faith are interrelated.

The authors adopt a multistudy approach to scaffold the exploration of mindfulness and religious faith as precursors of mindful consumption.

Study 1 carries out an experiment with undergraduates and demonstrates that mindfulness encourages mindful consumption. Study 2 conducts an offline survey with undergraduates and provides evidence that mindfulness and religious faith independently (i.e. without interacting with each other) encourage mindful consumption. Study 3 uses an online survey of consumers for conceptual replication and reaffirms the findings of Studies 1 and 2 across gender, occupations and household incomes (except middle-income households).

The implications of these findings are discussed, wherein mindfulness and religious faith are earmarked as viable avenues for promoting mindful consumption.

This seminal attempt uses multiple studies to empirically validate the nature and generalizability of relationships between mindfulness, religious faith and mindful consumption.

This paper aims to analyze the role and performance of different artificial intelligence (AI) algorithms in forecasting future movements in the main indices of the world’s largest stock exchanges.

Drawing on finance-based theory, an empirical and experimental study was carried out using four AI-based models. The investigation comprised training, testing and analysis of model performance using accuracy metrics and F1-Score on data from 34 indices, using 9 technical indicators, descriptive statistics, Shapiro–Wilk, Student’s t and Mann–Whitney and Spearman correlation coefficient tests.

All AI-based models performed better than the markets' return expectations, thereby supporting financial, strategic and organizational decisions. The number of days used to calculate the technical indicators enabled the development of models with better performance. Those based on the random forest algorithm present better results than other AI algorithms, regardless of the performance metric adopted.

The study expands knowledge on the topic and provides robust evidence on the role of AI in financial analysis and decision-making, as well as in predicting the movements of the largest stock exchanges in the world. This brings theoretical, strategic and managerial contributions, enabling the discussion of efficient market hypothesis (EMH) in a complex economic reality – in which the use of automation and application of AI has been expanded, opening new avenues of future investigation and the extensive use of technical analysis as support for decisions and machine learning.

The AI algorithms' flexibility to determine their parameters and the window for measuring and estimating technical indicators provide contextually adjusted models that can entail the best possible performance. This expands the informational and decision-making capacity of investors, managers, controllers, market analysts and other economic agents while emphasizing the role of AI algorithms in improving resource allocation in the financial and capital markets.

The originality and value of the research come from the methodology and systematic testing of the EMH through the main indices of the world’s largest stock exchanges – something still unprecedented despite being widely expected by scholars and the market.

Scanning projection-based stereolithography (SPSL) is a powerful technology for additive manufacturing with high resolution as well as large building area. However, the surface quality of stitching boundary in an SPSL system has been rarely studied, and no positive settlement was proposed to address the poor stitching quality. This paper aims to propose an approach of multi-pass scanning and a compensation algorithm for multi-pass scanning process to address the issue of poor stitching quality in SPSL systems.

The process of multi-pass scanning is realized by scanning regions repeatedly, and the regions can be cured simultaneously because of the very short repeat exposure time and very fast scanning. Then, the poor stitching quality caused by the non-simultaneous curing can be eliminated. Also, a compensation algorithm is designed for multi-pass scanning to reduce the stitching errors. The validity of multi-pass scanning is verified by curing depth test, while the performance of multi-pass scanning as well as proposed compensation algorithm is demonstrated by comparing with that of a previous SPSL system.

The results lead to a conclusion that multi-pass scanning with its compensation algorithm is an effective approach to improve the stitching quality of an SPSL system.

This study can provide advice for researchers to achieve the satisfactory surface finish with SPSL technology.

The authors proposed a process of multi-pass scanning as well as a compensation algorithm for SPSL additive manufacturing (system to improve the stitching quality, which has rarely been studied in previous work.

The newsvendor problem is fundamental to many operations management models. The problem focuses on the trade-off between the gains from satisfying demand and losses from unsold products. The newsvendor model and its extensions have been applied to various areas, such as production plan and supply chain management. This chapter examines the study about newsvendor problem. In this research, there is a review of the contributions for the multiproduct newsvendor problem. It focuses on the current literature concerning the mathematical models and the solution methods for the multiitem newsvendor problems with single or multiple constraints, as well as with the risks. The objective of this research is to go over the newsvendor problem and bring into comparison different newsvendor models applied to the flower industry. A few case studies are described addressing topics related to the newsvendor problem such as discounting and replenishment policies, inventory inaccuracies, or demand estimation. Three newsvendor models are put into practice in the field of flower selling. A full database of the flowers sold by an anonymous retailer is available for the study. Computational experiments for practical example have been conducted with use of the CPLEX solver with AMPL programming language. Models are solved, and an analysis of different circumstances and cases is accomplished.

The purpose of this paper is to investigate the effects of coupling agents on the structure and properties of the nanocomposite films and clarify their mechanism. Polyimide (PI)/Al₂O₃ nanocomposite films were prepared using different coupling agents.

Poly(amic acid) (PAA) was firstly synthesised from appropriate pyromellitic diannanocomposite and oxydianiline in N‐dimethylacetamide. Calculated amount of nano‐Al₂O₃ particles modified by different coupling agents (KH550, KH560, KH570 and AE3012) were added to PAA solution by an ultrasonic‐mechanical method and PI/nano‐Al₂O₃ film was fabricated by heat curing. The microstructure, thermal stability, mechanical properties and electric breakdown strength of the films were characterised.

The addition of coupling agents could greatly improve the dispersion homogeneity of Al₂O₃ nano‐particles in PI matrix. Results of corresponding characterisations indicated that both the thermal stability and mechanical properties of PI/Al₂O₃ nanocomposite film with KH550 were greater/better than others, while AE3012 could improve the electric breakdown strength.

In the present discussion, the effects of different coupling agents, KH550, KH560, KH570 and AE3012, were investigated. Results of this research work would be beneficial to an in‐depth understanding on the relationship between microstructure and properties of PI composites, and further promote the development of the high‐performance PI insulating materials.

The four coupling agents, KH550, KH560, KH570 and AE3012, were firstly used to disperse the nano‐Al₂O₃ particles in PI matrix. The effects of coupling agents on microstructure and properties of composites were discussed by the authors in detail.