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This study aims to focus on an adaptive method for fault detection and classification of fault types that trigger in three-phase transmission lines using artificial neural networks (ANNs). The proposed scheme can detect and classify several types of faults, including line-to-ground, line-to-line, double-line-to-ground, triple-line and triple-line-to-ground faults.

The fundamental components of three-phase current and voltage were used as inputs in the ANNs. An analysis of the impact of variations in the fault resistance, fault type and fault inception time was conducted to evaluate the ANNs performance. The survey compares the performance of the multi-layer perceptron neural network (MLPNN) and Elman recurrent neural network trained with the backpropagation learning technique to improve each of the three phases of the fault detection and classification process. A detailed analysis validates the choice of the ANNs architecture based on the variation in the number of hidden neurons in each step.

The mean square error, root mean square error, mean absolute error and linear regression are measured to improve the efficiency of the ANN models for both fault detection and classification. The results indicate that the MLPNN can detect and classify faults with a satisfactory performance.

The smart adaptive scheme is fast and accurate for fault detection and classification in a single circuit transmission line when faced with different conditions and can be useful for transmission line protection schemes.

The purpose of this study is the characteristics of the issue of legal responsibility for the smuggling of goods under the conditions of martial lawin Ukraine.

In the paper’s writing, the authors used an interdisciplinary approach, combining legal science and economics methods.

The detection and cessation of smuggling is currently a vital activity of customs authorities in the sphere of ensuring the financial and economic security of the state and a prerequisite for the stable development of market relations in Ukraine. At the same time, the lack of adequate legal responsibility for smuggling goods and the limited powers of customs authorities increased the facts of the smuggling of goods. This determines the importance of finding innovative principles of legal responsibility for smuggling goods under martial law conditions in Ukraine.

The paper aims to develop innovative principles of legal responsibility for smuggling goods under martial law in Ukraine.

Open innovation is an issue that has aroused great interest in recent years. The need to create an environment that facilitates the creation of ideas is essential for the implementation of a series of changes in organizational practices and routines that lead to the launch of new products. However, due to the more behavioral nature and the lesser externalization of these changes introduced in the company's internal processes, how this process occurs has not been studied in depth. The objective of this study is to analyze the effect of an open innovation climate on both incremental and radical product innovation. Moreover, it specifically analyzes the mediating role played by hidden innovation in this relationship.

The methodology used in this study was based on a survey of 213 Spanish SMEs, subsequently applying the structural equation methodology to contrast the results.

The results indicate that open innovation climate offers significant competitive advantages to SMEs. First, the open innovation climate in SMEs favorably influences product innovation (both incremental and radical). Secondly, it is observed that hidden innovations are essential to obtain product innovations. Finally, evidence of the mediating effect of hidden innovation has been obtained.

Although the literature often focuses on visible innovation, materialized in product development, this study demonstrates the importance of other types of innovations that are necessary to launch new products. This is especially relevant for SMEs that, with limited resources, must be creative enough to involve their personnel in introducing changes that will lead to new products. This paper attempts to strengthen the previous literature on hidden innovation by contributing to the understanding of how SMEs improve their innovative processes. However, the study has the limitations derived from using a single informant to obtain data, using subjective-type scales and being a cross-sectional research.

Managers of SMEs involved in innovation processes should favor the creation of an open innovation climate and invest in organizational innovation. Governments should promote policies to support hidden and open innovation.

The main interest of this work is based on the importance of hidden innovation for the development of innovations. This study shows how organizations must make a series of organizational changes prior to the implementation of more visible innovations materialized in products. For this task, the creation of a favorable climate for the development of new ideas becomes a fundamental task. On the other hand, this study has focused on SMEs, which tend to have fewer means for the development of the right conditions for innovation and are often more neglected by scientific research.

The authors estimated the hidden overhead (capital diversion or wasteful use of capital) of Vietnam state-owned enterprises (SOEs).

The authors used a panel data set of 10,200 Vietnam SOEs observed over the period 2010–2018. The authors modeled and estimated the hidden overhead by using a stochastic production frontier. The hidden overhead parameter is modelled as the technical inefficiency in the production function.

Vietnam SOEs are very capital intensive. The hidden overhead (or the wasteful use of capital) is very high with an average rate of 69%.

Alternative estimation methods should be used to account for endogeneity in production inputs. Lack of comparison with the Vietnam private firms.

The paper proposes an original way to quantify hidden overhead (or capital diversion) in the Vietnam SOEs. The finding (a capital diversion rate of 69% on average) is astonishing. It calls for an urgent and profound reform of the Vietnam SOEs.

Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.

Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.

First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.

This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.

This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

The purpose of this article is to better understand the relevance of value creation in the interconnection amongst entrepreneurship, marketing and innovation by reviewing the literature.

The authors employed a systematic review methodology using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol to analyse the literature in depth. The articles were selected from the Scopus database and dated from 1987 to 2021. An initial total of 1,158 articles was successively narrowed down to a final list of 123 papers matching the selection criteria. Moreover, content analysis on the sample was performed to explore and analyse whether value creation directly or indirectly appears as a goal or antecedent amongst entrepreneurship, marketing and innovation.

The findings suggest that the literature does not clearly define the topic linkage, and with the authors' results, the authors provide a comprehensive mapping of the contributions to a theoretical framework that synthesises knowledge. Moreover, the authors highlight that the interconnection between marketing and entrepreneurship, i.e. entrepreneurial marketing, requires an innovative approach for satisfying customer needs and creating value. Co-occurrence analysis of the keywords also allowed to identify four clusters that were open to new research streams.

Entrepreneurship, marketing and innovation are recognised research topics in the business and management literature. However, prior research has not provided clear and comprehensive evidence about how these three research topics are linked to each other. This work analyses the hidden relationship amongst them.

Intent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some generalization ability and benchmark its performance over other neural network models mentioned in this paper.

This study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore, the dataset Computer Science Literature Question (CSLQ) was constructed based on the Science and Technology Knowledge Graph. The datasets Airline Travel Information Systems, Snips (a natural language processing dataset of the consumer intent engine collected by Snips) and CSLQ were used for the empirical analysis. The accuracy of intent detection and F1 score of slot filling, as well as the semantic accuracy of sentences, were compared for several models.

The results showed that the proposed model outperformed all other benchmark methods, especially for the CSLQ dataset. This proves that the design of this study improved the comprehensive performance and generalization ability of the model to some extent.

This study contributes to the understanding of question sentences in a specific domain. LSTM was improved, and a computer literature domain dataset was constructed herein. This will lay the data and model foundation for the future construction of a computer literature question answering system.

Artificial intelligence (AI) is transforming consumers' experiences and how firms identify, create, nurture and manage interactive marketing relationships. However, most marketers do not have a clear understanding of what AI is and how it may mutually benefit consumers and firms. In this paper, the authors conduct an extensive review of the marketing literature, develop an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships, identify research gaps and offer a future research agenda.

The authors first conduct an extensive literature review in 16 top marketing journals on AI. Based on this review, an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships was conceptualized.

The literature review led to a number of key research findings and summary areas: (1) an historical perspective, (2) definitions and boundaries of AI, (3) AI and interactive marketing, (4) relevant theories in the domain of interactive marketing and (5) synthesizing AI research based on antecedents to AI usage, interactive AI usage contexts and AI-enabled value co-creation outcomes.

This is one of the most extensive reviews of AI literature in marketing, including an evaluation of in excess or 300 conceptual and empirical research. Based on the findings, the authors offer a future research agenda, including a visual titled “What is AI in Interactive Marketing? AI design factors, AI core elements & interactive marketing AI usage contexts.”

The time-varying equivalent linear temperature gradient (ELTG) significantly affects the development of faulting and must therefore be accounted for in pavement design. The same is true for faulting of bonded concrete overlays of asphalt (BCOA) with slabs larger than 3 x 3 m. However, the evaluation of ELTG in Mechanistic-Empirical (ME) BCOA design is highly time-consuming. The use of an effective ELTG (EELTG) is an efficient alternative to calculating ELTG. In this study, a model to quickly evaluate EELTG was developed for faulting in BCOA for panels 3 m or longer in size, whose faulting is sensitive to ELTG.

A database of EELTG responses was generated for 144 BCOAs at 169 locations throughout the continental United States, which was used to develop a series of prediction models. Three methods were evaluated: multiple linear regression (MLR), artificial neural networks (ANNs), and multi-gene genetic programming (MGGP). The performance of each method was compared, considering both accuracy and model complexity.

It was shown that ANNs display the highest accuracy, with an R² of 0.90 on the validation dataset. MLR and MGGP models achieved R² of 0.73 and 0.71, respectively. However, these models consisted of far fewer free parameters as compared to the ANNs. The model comparison performed in this study highlights the need for researchers to consider the complexity of models so that their direct implementation is feasible.

This research produced a rapid EELTG prediction model for BCOAs that can be incorporated into the existing faulting model framework.