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The purpose of this paper is to present research in detecting and identifying abrupt faults in controlled auto‐regressive (CAR) processes.

Model‐based approach is adopted in this paper. Two series of fault‐tolerant iterative estimators are set up to estimate online the coefficients in a CAR process. Based on these fault‐tolerant estimators, the detailed detecting and identifying algorithms are obtained for not only the pulse‐type faults but also the step‐type faults in CAR process.

This paper illustrates the useful information that can be obtained from residuals and that can be used to detect pulse‐type faults as well as step‐type faults. A fault‐tolerant recursive estimator for the coefficients of the CAR process is put forward. Using a simple transformation from step‐ to pulse‐type faults, all kinds of diagnosis methods to detect and identify step‐type faults can be used.

Fault‐tolerant estimators and fault detection and identification algorithms are aimed at abrupt faults in CAR processes.

Most of the algorithms given in this paper can be used in many different fields, such as process monitoring, safety control and change detection, etc.

This paper contributes to research of abrupt faults and abrupt changes in a CAR process and emphasizes identification of magnitudes of abrupt faults. The fault‐tolerant estimators are effective not only to detect faults but also to identify safely the coefficients CAR model.

This study aims to develop and validate a scale for measuring online fashion brand recognition (OFBR).

Given the existing literature on brand, multiple stages of studies were conducted as a part of the scale development process. The OFBR scale was validated by a step-by-step process following the scale validation methodology suggested in the specialised literature.

The results demonstrate the 25 items of five unique factors that form the OFBR construct and confirm the strong validity of the construct. This finding suggests that the five-dimensional approach (online brand familiarity, online visual simplicity, online aesthetic attraction, online brand emotion and online social reputation) plays important role to form OFBR.

A valid and reliable OFBR scale provides a foundation for broadening the understanding on the important constructs that form OFBR, which is essential for online fashion retailing. Knowledge of the crucial antecedents that influence consumers towards online fashion products can enhance marketers’ capability to position their brands towards their target markets.

The relevance of this study lies in validating the scale for measuring OFBR for the first time in the literature on online fashion brand.

Este estudio tiene como objetivo desarrollar y validar una escala para medir el reconocimiento de marcas de moda online (OFBR).

Partiendo de la literatura de marca, se llevaron a cabo múltiples estudios como parte del proceso de desarrollo de la escala. La escala OFBR fue validada en un proceso de diversas etapas siguiendo la metodología de validación de escalas sugerida en la literatura especializada.

Los resultados demuestran los veinticinco ítems de cinco factores únicos que forman el constructo OFBR y confirman la validez del constructo. Este hallazgo sugiere que el enfoque de cinco dimensiones (familiaridad con la marca online, simplicidad visual online, atracción estética online, emoción de la marca online y reputación social online) juega un papel importante en la formación de la OFBR.

Una escala OFBR válida y fiable proporciona una base para ampliar la comprensión de los constructos importantes que forman OFBR, lo cual es esencial para la venta de moda online. Conocer los antecedentes que condicionan al consumidor al comprar productos de moda online puede mejorar la capacidad del vendedor para posicionar su marca en el mercado objetivo.

El interés de este estudio radica en la validación de la escala para medir la OFBR por primera vez en la literatura sobre la marca de moda online.

Artículo de investigación

At present, the enzyme washing process is widely used in hemp garment in the industry. However, it has many disadvantages. First, it is inefficient and labor-intensive, which can only be produced in small quantities and will take much time for workers to check. Second, its cost is high. The strength loss of the garment is up to 20 percent, with a bad damage. Presently, the enzyme washing process of the gray fabric is not industrialized. The purpose of this paper is to obtain a stable and efficient process for the enzyme washing of the gray fabric.

The whiteness, weight loss rate, strength, dyeing property and fabric style of enzyme washed gray fabrics were studied.

The results showed that the enzyme treated fabric has soft handle, smooth surface, good elasticity and high levelness, as well as low strength reduction rate under the optimized condition of bath ratio of 1:12, cellulase dosage of 0.3 percent, pectinase dosage of 1 g/L, treatment temperature of 55°C, treatment time of 80 min and pH of 6.

First, the process is environment friendly, green and sustainable and, second, it will make the industrialization of the enzyme washing process of gray fabrics, with a high commercial value.

The aim of this paper is to investigate the factors influencing citizens' willingness to participate in the development of smart cities.

Citizens drawn from 30 second-tier cities in China were chosen as the research object for this empirical research. Based on citizenship behavior theory, research hypotheses were tested and analyzed using structural equation modeling (SEM).

The results indicated that information publicity has a direct and positive effect on residents' participation behavior. Perceived benefits, personal responsibility and subjective norms are positively associated with residents' citizenship. Additionally, citizenship was found to affect residents' participation intention positively. Finally, the moderating effect of information credibility in this context was also verified.

As one of the first empirical studies on this topic, this paper offers important guidance for future research regarding residents' participation in the development of smart cities. On this basis, the implications of this research with respect to policies that aim to encourage residents to participate in the construction of smart cities are discussed.

The purpose of this research is to investigate the millennial consumers’ purchasing behavior of fashion clothing brands in the spectrum of brand awareness, brand schematicity with brand consciousness and brand nationality. This study mainly considered the millennial consumers as they are the most dynamic and discerning segment when choosing a distinct fashion brand.

The study uses a well-structured questionnaire developed and distributed to 266 millennial respondents using shoping malls and university campus intercept methods. Data were analyzed by applying the structural equation modeling.

The results show that brand awareness has a mediating effect on the millennials’ purchasing behavior toward fashion clothing brands. The study also confirms the active moderation role of brand schematicity on the purchasing behavior toward fashion clothing brands.

Based on these findings, the fashion clothing retailers should aim to maximize their sales from the millennial segments by enhancing brand awareness. The schematic consumers are more engaged in the numbers of quality hints to make their choice on the fashion clothing brands. Therefore, the practitioners must consider such information, and that should be available in the fashion clothing retail outlets.

The study contributes to the existing literature of the millennials’ purchasing behavior toward the fashion clothing brands. Moreover, research on this segment related to brand awareness and brand schematicity is insufficient, and the current study may add significant value.

The purpose of this study is to investigate the relationship between artificial intelligence (AI) and decision making in the development of AI-related capabilities. We investigate if and how AI-driven decision making has an impact on firm performance. We also investigate the role played by environmental dynamism in the development of AI capabilities and AI-driven decision making.

We surveyed 346 managers in the United States using established scales from the literature and leveraged p modelling to analyse the data.

Results indicate that AI-driven decision making is positively related to firm performance and that big data-powered AI positively influences AI-driven decision making. Moreover, there is a positive relationship between big data-powered AI and the development of AI capability within a firm. It is also found that the control variables of firm size and age do not significantly affect firm performance. Finally, environmental dynamism does not have a positive and significant moderating effect on the path connecting big data-powered AI and AI-driven decision making, while it exerts a positive moderating effect on the development of AI capability to strengthen AI-driven decision making.

These findings extend the resource-based view by highlighting the capabilities developed within the firm to manage big data-powered AI. This research also provides theoretically grounded guidance to managers wanting to align their AI-driven decision making with superior firm performance.

This study investigates the profound impact of artificial intelligence (AI) capabilities on decision-making processes and organizational performance, addressing a crucial gap in the literature by exploring the mediating role of decision-making speed and quality.

Drawing upon resource-based theory and prior research, this study constructs a comprehensive model and hypotheses to illuminate the influence of AI capabilities within organizations on decision-making speed, decision quality, and, ultimately, organizational performance. A dataset comprising 230 responses from diverse organizations forms the basis of the analysis, with the study employing a partial least squares structural equation model (PLS-SEM) for robust data examination.

The results demonstrate the pivotal role of AI capabilities in shaping organizational decision-making processes and performance. AI capability significantly and positively affects decision-making speed, decision quality, and overall organizational performance. Notably, decision-making speed is a critical factor contributing significantly to enhanced organizational performance. The study further uncovered partial mediation effects, suggesting that decision-making processes partially mediate the relationship between AI capabilities and organizational performance through decision-making speed.

This study contributes to the existing body of literature by providing empirical evidence of the multifaceted impact of AI capabilities on organizational decision-making and performance. Elucidating the mediating role of decision-making processes advances our understanding of the complex mechanisms through which AI capabilities drive organizational success.

In the era of the digital economy, higher demands are placed on versatile talents, and the cultivation of students with innovative and entrepreneurial abilities has become an important issue for the further development of higher education, thus leading to extensive and in-depth research by many scholars. The study summarizes the characteristics and patterns of dual-innovation education at different stages of development, hoping to provide a systematic model for the development of dual-innovation education in China and make up for the shortcomings.

This paper uses Citespace software to visualize and analyze the relevant literature in CNKI and Web of Science databases from a bibliometric perspective, focusing on quantitative analysis in terms of article trends, topic clustering, keyword co-linear networks and topic time evolution, etc., to summarize and sort out the development of innovation and entrepreneurship education research at home and abroad.

The study found that the external characteristics of the literature published in the field of bi-innovation education in China and abroad are slightly different, mainly in that foreign publishers are more closely connected and have formed a more stable ecosystem. In terms of research hotspots, China is still in a critical period of reforming its curriculum and teaching model, and research on the integration of specialization and creative education is in full swing, while foreign countries focus more on the cultivation of students' entrepreneurial awareness and the enhancement of individual effectiveness. In terms of cutting-edge analysis, the main research directions in China are “creative education”, “new engineering”, “integration of industry and education” and “rural revitalization”.

Innovation and entrepreneurship education in China is still in its infancy, and most of the studies lack an overall overview and comparison of foreign studies. Based on the econometric analysis of domestic and foreign literature, this paper proposes a path for domestic innovation and entrepreneurship education reform that can make China's future education reform more effective.

This study investigates the effect of consumers' brand attitude changes according to the fashion film type. Furthermore, it examines the psychological mechanism by engagement and consumer fantasy proneness. This study is meaningful because it provides a more in-depth understanding of the use of fashion film as a means of consumer-oriented persuasion communication.

This research uses a 2(fashion film type: narrative vs non-narrative) × 2(consumer fantasy proneness: high vs low) mixed factorial design to test the hypotheses. ANOVA and the PROCESS macro mounted on SPSS was used to test hypotheses.

The group with high consumer fantasy proneness showed more changes in brand attitude when exposed to non-narrative than narrative fashion films, but the group with low consumer fantasy proneness showed no significant difference in brand attitude change according to the fashion film type. In addition, when consumer fantasy proneness is high, media and brand engagement for non-narrative fashion films increase sequentially, resulting in a greater change in brand attitude, whereas these psychological mechanisms do not work in groups with low consumer fantasy proneness.

Fashion brands should identify their respective target group when producing fashion films and choose differentiated narrative forms. In the case of pursuing a fantastic aesthetic value, the non-narrative type induces more attention and curiosity from consumers than the narrative type, which affects the feeling of a special bond or relevance with the brand.

This study has value in that it demonstrates the rationale for why a fashion brand needs to select a differentiated content structure according to the aesthetic value pursued when making a fashion film in branding work.

The aim of this study is to empirically investigate the impact of marketing analytics capability on business performance from the perspective of RBV theory.

This study used a survey method to gather information from 225 food processing SMEs registered with the Ghana Enterprise Agency (GEA) in Ghana’s eastern region. A structural equation modeling (SEM) path analysis was used to assess the impact of marketing analytics capability (MAC) on the performance of SMEs.

The results of the study show that MAC significantly and positively affect the financial performance (FP), customer performance (CF), internal business process performance (IBPP) and learning and growth performance (LGP) of Ghanaian SMEs. The findings of this study also illustrated the significance of MAC determinants, including marketing analytics skills (MAS), data resource management (DRM) and data processing capabilities (DPC), in achieving SME success in Ghana.

The research’s conclusions give RBV theory strong credence. The results of this study also provide credence to previous research finding that SMEs should view MAC and its determinants (i.e. DRM, DPC, MAS) as a crucial strategic capability to improve their performance (i.e. FP, CF, IBPP, LGP). With regard to its contribution, this study broadens the body of knowledge on MAC and SME performance, particularly in the context of an emerging economy.