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This study aims to build a theoretical model including intermediate-level outside-in marketing capabilities (ILOIMC), radical and incremental technological innovations and management innovation.

This research used 272 pairs of survey questionnaires from Chinese firms’ managers to examine the hypotheses.

The results indicate that ILOIMC enhance management innovation by stimulating radical technological innovation. Furthermore, the mediating effect of incremental technological innovation depends on technological turbulence.

This study may have several limitations which future research could try to overcome: cross-sectional data, Chinese samples, exclusive focus on ILOIMC, sociotechnical approach to innovation typology and measuring ILOIMC as a first-order variable.

ILOIMC can significantly improve innovations in technology and management systems by using customer value and market information.

This study proposes a new taxonomy to classify marketing capabilities into lower-level inside-out marketing capabilities, ILOIMC and higher-level outside-in marketing capabilities. It also provides an explicit discussion and examination of the influence of ILOIMC on technological and management innovations and the contingency effect of technological turbulence. Thus, it responds to Musarra and Morgan’s (2020) call for more research into the mechanism that explains when (the conditions under which) and how (the process by which) outside-in marketing capabilities could contribute to firm innovation.

This study aims to examine whether consumers’ lay theories of emotion play a moderating role between self-threat and their choice of threat-coping strategies (direct resolution and escapism) and product preference.

The present research uses the methods of experimental design and surveys to collect data and verify the hypotheses we assumed.

Study 1 indicates that in self-threatening situations, people who perceive emotions as fleeting (lasting) are more likely to use a threat-coping strategy of direct resolution (escapism). Study 2 demonstrates that people who believe emotions are fleeting are more likely to choose problem-solving products; people who believe emotions are lasting are more likely to choose emotion-enhancing products. Study 3 further demonstrates that the direct resolution (escapism) strategy plays a mediating role between the interaction effect and consumer preference for problem-solving products (emotion-enhancing products). Study 4 replicates the results of Study 2 by incorporating the manipulation of lay theories of emotion transience in a product evaluation context.

A limitation of the present research is that this paper puts focus on exploring the effects of self-view threat (e.g. intelligence and competence) on consumer product preferences. Another issue for future research is the extent to which emotion-transience theories hold for specific emotions. Given that distinct emotions of the same valence differ in their antecedent appraisals and that specific emotion could lead to different subsequent behaviors (Lerner and Keltner, 2000), future research may need to explore the roles of specific negative emotions triggered by self-threat in consumers’ product choosing behaviors. One potential direction for future research is to examine whether the perceived locus of control affects consumers’ choice of threat-coping strategies and product preferences.

Marketers could use product tactics for motivating consumers to restore their self-perceptions on the threatened attributes and address the self-threat, such as product attributes, advertising copy or promotional appeals that insert people who are more motivated to directly resolve the threat. Marketers can nudge consumers toward a direct resolution strategy by posting prompts such as, “I can do it!” For example, the slogan of Nike – “Just do it” and the 2012 award-winning campaign by Nike Spain have told consumers: “If something is burning you up, burn it up by running” (Allard and White, 2015), which suggests that consumers experiencing self-threat may resolve the negative self-discrepancy through the acquisition of the products in the advertisement. Another important implication suggested by the findings is that product consumption can be a way of helping consumers escape from self-threats. For example, the slogan of Coca-Cola – “Taste the feeling” resonates with consumers and stimulates their basic hedonic needs.

First, this research extends previous research by demonstrating that lay theories of emotion serve as a motivator of the selection of threat-coping strategies. Second, this research is conducive for literature to examine how differences in lay theories of emotion affect consumers’ product-choosing behaviors to cope with self-discrepancies. Third, the present research extends the broad marketing literature by differentiating problem-solving products from emotion-enhancing products.

To realize classification of different pavements, a road roughness acquisition system design and an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation algorithm for road roughness detection is presented in this paper. The developed measurement system, including hardware designs and algorithm for software, constitutes an independent system which is low-cost, convenient for installation and small.

The inputs of restricted Boltzmann machine deep neural network are the vehicle vertical acceleration power spectrum and the pitch acceleration power spectrum, which is calculated using ADAMS finite element software. Adaboost Backward Propagation algorithm is used in each restricted Boltzmann machine deep neural network classification model for fine-tuning given its performance of global searching. The algorithm is first applied to road spectrum detection and experiments indicate that the algorithm is suitable for detecting pavement roughness.

The detection rate of RBM deep neural network algorithm based on Adaboost Backward Propagation is up to 96 per cent, and the false positive rate is below 3.34 per cent. These indices are both better than the other supervised algorithms, which also performs better in extracting the intrinsic characteristics of data, and therefore improves the classification accuracy and classification quality. Additionally, the classification performance is optimized. The experimental results show that the algorithm can improve performance of restricted Boltzmann machine deep neural networks. The system can be used for detecting pavement roughness.

This paper presents an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation for identifying the road roughness. Through the restricted Boltzmann machine, it completes pre-training and initializing sample weights. The entire neural network is fine-tuned through the Adaboost Backward Propagation algorithm, verifying the validity of the algorithm on the MNIST data set. A quarter vehicle model is used as the foundation, and the vertical acceleration spectrum of the vehicle center of mass and pitch acceleration spectrum were obtained by simulation in ADAMS as the input samples. The experimental results show that the improved algorithm has better optimization ability, improves the detection rate and can detect the road roughness more effectively.

This study explores the influencing factors affecting smart supply chain innovation (SSCI) performance of commodity distribution enterprises, and proposes the corresponding framework from the perspective of the application of technology to improve the SSCI performance and make up the research gap in this field.

A multi-case study method is adopted in this study. Four distribution commodity distribution enterprises A, B, C and D in China are chosen as case enterprises. The interviews with senior management team members are used to collect data. The combination of open coding and axial coding are used to process the data. By testing the reliability and validity, the theoretical framework is summarized.

First, we find that the technology application cost inhibits SSCI and that the level of technology suitable for enterprise development will promote SSCI. Second, SSCI in structure, management and services can improve the performance and innovation ability of enterprises. Third, the quality of multi-channel integration and degree of customization around customer demand can significantly modify the above effects.

Compared with previous studies, this study reveals for the first time the correlation between the SSCI performance and technology application, SSCI in structure, management and service, providing new ideas for relevant researches on SSCI, and providing new theoretical support for managers' decision-making related to SSCI.

The purpose of this paper is to examine the interesting but largely unexamined impact of self-uncertainty on the persuasiveness of self-deprecating advertisement.

In the present research, the experimental design and survey methods are used to collect data. Furthermore, the ANOVA and bootstrap analysis methods are adopted to verify whether a self-deprecating advertisement is more persuasive for consumers experiencing self-uncertainty and explore the mediating role of self-verification.

Study 1 indicates that people experiencing self-uncertainty are more likely to engage in actual self-verification. Study 2 demonstrates that consumers experiencing self-uncertainty are more likely to purchase products in a self-deprecating advertisement (vs. self-enhancing advertisement), and actual self-verification motive underlies this effect. In Study 3, a novel boundary condition for the main effect–product type (hedonic vs. utilitarian) is found, and it further reveals that the impact of self-uncertainty on the persuasiveness of self-deprecating advertisement will attenuate when the advertised product is utilitarian.

This research reveals that self-deprecating advertising is more desirable for consumers who experience self-uncertainty. Based on the conclusions in this paper, the self-deprecating advertising is more attractive and desirable for consumers who are reminded about their personal uncertainties. Thus, marketers could employ self-deprecating (vs. self-enhancing) advertisement to promote products. For example, in order to promote the waterproof function of iPhone 12, Apple China released a self-deprecating advertisement of “Splash proof and water resistant. Don't worry, iPhone.”

First, this research not only sheds new light on the relationship between self-uncertainty and the persuasiveness of self-deprecating advertisement but also verifies the mediating role of self-verification motive in this relationship. Moreover, this research reveals that self-uncertainty is a significant factor in how people react to the self-deprecating advertisement. It is noteworthy that the self-uncertainty effect is more likely to be found when the advertised product is related to hedonic or experiential consumption as opposed to utilitarian consumption.

Sandwich structures with well-designed cellular cores exhibit superior shock resistance compared to monolithic structures of equal mass. This study aims to develop a comprehensive analytical model for predicting the dynamic response of cellular-core sandwich structures subjected to shock loading and investigate their application in protective design.

First, an analytical model of a clamped sandwich beam for over-span shock loading was developed. In this model, the incident shock-wave reflection was considered, the clamped face sheets were simplified using two single-degree-of-freedom (SDOF) systems, the core was idealized using the rigid-perfectly-plastic-locking (RPPL) model in the thickness direction and simplified as an SDOF system in the span direction. The model was then evaluated using existing analytical models before being employed to design the sandwich-beam configurations for two typical engineering applications.

The model effectively predicted the dynamic response of sandwich panels, especially when the shock-loading pulse shape was considered. The optimal compressive cellular-core strength increased with increasing peak pressure and shock-loading impulse. Neglecting the core tensile strength could result in an overestimation of the optimal compressive cellular-core strength.

A new model was proposed and employed to optimally design clamped cellular-core sandwich-beam configurations subjected to shock loading.

The purpose of this paper is to propose a condition-based opportunistic maintenance policy considering economic dependence for a series–parallel hybrid system with a K-out-of-N redundant structure, where a single component in series is denoted as subsystem1, and K-out-of-N redundant structure is denoted as subsystem2.

Based on the theory of Residual Useful Life (RUL), inspection points are determined, and then different maintenance actions are adopted in the purpose of minimizing the cost rate. Both perfect and imperfect maintenance actions are carried out for subsystem1. More significantly, regarding economic dependence, condition-based opportunistic maintenance is designed for the series–parallel hybrid system: preemptive maintenance for subsystem1, and both preemptive and postponed maintenance for subsystem2.

The sensitivity analysis indicates that the proposed policy outperforms two classical maintenance policies, incurring the lowest total cost rate under the context of both heterogeneous and quasi-homogeneous K-out-of-N subsystems.

This model can be applied in series–parallel systems with redundant structures that are widely used in power transmission systems in electric power plants, manufacturing systems in textile factories and sewerage systems. Considering inconvenience and high cost incurred in the inspection of hybrid systems, this model helps production managers better maintain these systems.

In maintenance literature, much attention has been received in repairing strategies on hybrid systems with economic dependence considering preemptive maintenance. Limited work has considered postponed maintenance. However, this paper uses both condition-based preemptive and postponed maintenance on the issue of economic dependence bringing opportunities for grouping maintenance activities for a series–parallel hybrid system.

This study aimed to investigate the relationship between spirituality, including religious spirituality (i.e. supernatural beliefs) and secular spirituality (i.e. social beliefs), and mental health among police trainees.

Participants in this study were police trainees of a police academy. An online survey was conducted to measure spirituality and mental health among these police trainees. The association between spirituality and mental health was analyzed using hierarchical linear regression and hierarchical logistic regression with demographic variables (i.e. gender and age) controlled for.

The results revealed that the police trainees with stronger secular spirituality tended to have better general mental health. Higher levels of secular spirituality were significantly associated with lower levels of mental illness risk and suicidal ideation. By contrast, religious spirituality was not significantly related to police trainees' mental health.

The present study is the first to empirically investigate the relationship between spirituality and mental health among police trainees. The findings may be enlightening for future research on the mental health of police officers and trainees, and provide novel perspectives and pragmatic implications for the development of spirituality-based prevention strategies and intervention programs for enhancing the mental health and well-being of the police.

In view of the significant changes in the capital structure of China’s real estate industry and enterprises in recent years, this chapter employs financial indicators and the linear regression function to analyze the relationship between corporate debt ratio and the performance of 111 A-share listed real estate enterprises in China. This study finds that the corporate debt ratio of China’s real estate enterprises in the past decade has a significant negative impact on enterprises’ performance. The study also finds that among China’s real estate companies, the corporate debt ratio has a more significant negative impact on the performance of non-state-owned enterprises than state-owned enterprises. In addition, a high debt ratio has a more significant negative impact on return on equity (ROE) than on return on assets (ROA). However, when Tobin’s Q serves as a proxy for firm performance, the negative impact of the corporate debt ratio becomes insignificant in the presence of the firm size factor. The research results of this chapter can provide some reference for subsequent policy-making and investment decisions in the Chinese real estate market.

Considering the problem that the high recognition rate of deep learning requires the support of mass data, this study aims to propose an insulating fault identification method based on small data set convolutional neural network (CNN).

Because of the chaotic characteristics of partial discharge (PD) signals, the equivalent transformation of the PD signal of unit power frequency period is carried out by phase space reconstruction to derive the chaotic features. At the same time, geometric, fractal, entropy and time domain features are extracted to increase the volume of feature data. Finally, the combined features are constructed and imported into CNN to complete PD recognition.

The results of the case study show that the proposed method can realize the PD recognition of small data set and make up for the shortcomings of the methods based on CNN. Also, the 1-CNN built in this paper has better recognition performance for four typical insulation faults of cable accessories. The recognition performance is improved by 4.37% and 1.25%, respectively, compared with similar methods based on support vector machine and BPNN.

In this paper, a method of insulation fault recognition based on CNN with small data set is proposed, which can solve the difficulty to realize insulation fault recognition of cable accessories and deep data mining because of insufficient measure data.