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The purpose of this paper is to empirically investigate the impact of board characteristics such as size, independence, busyness and duality on the extent of discretionary disclosures of listed Indian firms.

A disclosure index with 110 items was constructed to assess the discretionary disclosures in the annual reports of listed firms. The study measured disclosure using 1,024 firm-year observations over 8 years from 2009 to 2016. Board characteristics such as size, independence, busyness and duality have been used in the study as indicators of corporate governance.

The results indicate that while the proportion of independent directors positively impacts the extent of discretionary disclosures, boards with duality and the busyness of the director have a negative impact. The size of the board does not significantly impact the extent of disclosures.

This study examines the discretionary disclosures made only in the annual reports. Future studies could examine information disclosed in other media. Moreover, this study uses an un-weighted self-constructed disclosure index, which is subject to its inherent limitations.

This study has examined the impact of the “busyness” of the director on the extent of disclosures. This variable has not been explored in prior studies. The significance of the variable indicates that the number of directorships held impacts the efficiency with which a director performs his/her role in the board. The study reiterates the need for firms and policymakers to focus on improving board independence and to move away from leadership structures with duality.

A new Chatbot system is implemented to provide both voice-based and textual-based communication to address student queries without any delay. Initially, the input texts are gathered from the chat and then the gathered text is fed to pre-processing techniques like tokenization, stemming of words and removal of stop words. Then, the pre-processed data are given to the Natural Learning Process (NLP) for extracting the features, where the XLnet and Bidirectional Encoder Representations from Transformers (BERT) are utilized to extract the features. From these extracted features, the target-based fused feature pools are obtained. Then, the intent detection is carried out to extract the answers related to the user queries via Enhanced 1D-Convolutional Neural Networks with Long Short Term Memory (E1DCNN-LSTM) where the parameters are optimized using Position Averaging of Binary Emperor Penguin Optimizer with Colony Predation Algorithm (PA-BEPOCPA). Finally, the answers are extracted based on the intent of a particular student’s teaching materials like video, image or text. The implementation results are analyzed through different recently developed Chatbot detection models to validate the effectiveness of the newly developed model.

A smart model for the NLP is developed to help education-related institutions for an easy way of interaction between students and teachers with high prediction of accurate data for the given query. This research work aims to design a new educational Chatbot to assist the teaching-learning process with the NLP. The input data are gathered from the user through chats and given to the pre-processing stage, where tokenization, steaming of words and removal of stop words are used. The output data from the pre-processing stage is given to the feature extraction phase where XLnet and BERT are used. In this feature extraction, the optimal features are extracted using hybrid PA-BEPOCPA to maximize the correlation coefficient. The features from XLnet and features from BERT were given to target-based features fused pool to produce optimal features. Here, the best features are optimally selected using developed PA-BEPOCPA for maximizing the correlation among coefficients. The output of selected features is given to E1DCNN-LSTM for implementation of educational Chatbot with high accuracy and precision.

The investigation result shows that the implemented model achieves maximum accuracy of 57% more than Bidirectional long short-term memory (BiLSTM), 58% more than One Dimansional Convolutional Neural Network (1DCNN), 59% more than LSTM and 62% more than Ensemble for the given dataset.

The prediction accuracy was high in this proposed deep learning-based educational Chatbot system when compared with various baseline works.

The aim of this paper is to contribute to the services marketing literature by developing a scale based on Parasuraman’s SERVQUAL scale for the measurement of distributor perceived service quality at the distributor–manufacturer interface of the pharmaceutical supply chain.

Based on a literature review and discussions with experts, a questionnaire was designed basing on the widely used service quality measurement scale (SERVQUAL). Personal survey was conducted among selected distributors spread over three major cities of the Indian pharmaceutical market. The study used the exploratory factor analysis to identify the critical factors of service quality followed by the confirmatory factor analysis (AMOS 20).

A valid scale with four dimensions – (reliability, assurance, responsiveness and communication) and 13 items for measuring the distributor perceived service quality was developed which also satisfied all the reliability and validity tests. The findings of the present study indicate that distributor perceived service quality has an effect on satisfaction.

The proposed scale is an attempt to explore the less researched area. This study will give further insights to researchers to measure service quality at different phases of the pharmaceutical supply chain. The study is limited to three cities; it can be extended to other regions of the country. This study will be helpful to the practicing managers to measure the service quality and improve the performance in the pharmaceutical supply chain.

Service quality in pharmaceutical supply chain is very important, as it directly effects the health of the people, so the proposed scale can be used to control the quality of service.

The scale developed in this study can also be used for measuring distributor perceived service quality in other manufacturing sectors. This research provides direction and scope for further research to develop new concepts and models in measuring service quality in the supply chain.

The external business environment of the organization is always changing at a rapid pace. For a firm to adapt to changing client requirements, it must implement the right business procedures and strategies. To improve competitive advantage, this study investigates the roles that supply chain partnerships, cross-functional integration, responsiveness and resilience play in achieving competitive advantages in Palestine.

Industrial institutions in Palestine constitute the study population. Data are collected by distributing surveys via Google Forms linked to manufacturers in industries such as the Leather and shoe Industry, metal industries, chemical industries, construction industries, textile industries, stone and marble industries, pharmaceutical industry, veterinary industry, food industry, plastic industry, paper industry, major advantages and disadvantages. The SEM-PLS approach is used to analyze the data.

The findings demonstrate that supply chain responsiveness, resilience and cooperation are all improved by cross-functional integration in inventory data integration and immediate operation. Supply chain partnerships improve the supply chain’s responsiveness, resilience and competitive advantage by involving partners in work teams and exchanging best practices. The enhancement of supply chain resilience and competitive advantage is influenced by the company’s capacity to act promptly in response to variations in demands.

This paper faces some limitations and it can be drawn as follows: To enhance supply chain risk management, the study continues to concentrate on manufacturing organizations that have internal integration. It also emphasizes the necessity of supply chain integration, which establishes direct connections with outside partners.

The findings of this study suggest some policy implications, as follows: To provide the manufacturing sector with a competitive edge, operations supervisors must be able to track and assess processes to ensure they are meeting demand. Firms that possess the ability to adjust to novel procedures or advancements in technology gain a competitive edge by guaranteeing consistent and high-quality delivery of products.

By implementing IT integration, this study theoretically and practically advances the understanding of the resource-based view of competitive advantages. This study focuses on providing insights into the nature of the relationship between supply chain partnership, cross-functional integration, responsiveness and flexibility and competitive advantages in the manufacturing sector in the Palestinian market.

To enhance the performance transmit antenna selection (TAS) of spatial modulation (SM), systems technique needs to be essential. This TAS is an effective technique for reducing the multiple input multiple output (MIMO) systems computational difficulty, and bit error rate (BER) can increase remarkably by various TAS algorithms. But these selection methods cannot provide code gain, so it is essential to join the TAS with external code to obtain cy -ode gain advantages in BER.

In this paper, Bose–Chaudhuri–Hocquenghem (BCH)-Turbo code TC is combined with the orthogonal space time block code system.

In some existing work, the improved BER has been perceived by joining forward error correction code and space time block code (STBC) for MIMO systems provided greater code gain. The proposed work can provide increasing code gain and the effective advantages of the TAS-OSTBC system.

To perform the system analysis, Rayleigh channel is used. In the case with multiple TAS-OSTBC systems, better performance can provide by this new joint of the BCH-Turbo compared to the conventional Turbo code for the Rayleigh fading.

The Internet of Things (IoT), as one of the new digital technologies, has created wide applications in various industries, and one of the most influential industries of this technology is the transportation industry. By integrating the IoT with the transportation industry, there will be dramatic changes in the industry, and it will provide many entrepreneurial opportunities for entrepreneurs to develop new businesses. Opportunity identification is at the heart of the entrepreneurial process, and entrepreneurs identify innovative goods or services to enter a new market by identifying, evaluating, and exploiting opportunities. Despite the desire of transportation managers to invest in the IoT and the increase in research in this area, limited research has focused on IoT-based entrepreneurial opportunities in the transportation industry. Therefore, the present study aims to identify IoT-based entrepreneurial opportunities in the transportation industry and examine their importance.

To achieve the research objective, the authors applied a mixed approach. First, adapting the lens of the industry value chain theory, a comprehensive literature review, besides a qualitative approach including semi-structured interviews with experts and thematic analysis, was conducted to identify the entrepreneurial opportunities. The identified opportunities were confirmed in the second stage using a quantitative survey method, including the Student t-test and factor analysis. Finally, the identified opportunities were weighted and ranked using the best worst method (BWM).

Entrepreneurial opportunities are classified into five main categories, including “smart vehicles”, “business partners/smart transportation supply side”, “supporting services”, “infrastructures”, and “smart transport management and control”. The infrastructures group of opportunities ranked the highest amongst the identified groups.

This study adds to the digital entrepreneurship opportunity recognition literature by addressing opportunities in a smart industry propelled by digital technologies, including developing new products or new applications of the available technologies. Additionally, inspired by the industry value chain theory, this article develops a framework including various digital entrepreneurial opportunity networks which are necessary to add value to any industry and, thus, could be applied by entrepreneurs to recognize opportunities for new intermediaries to enter other digital-based industries. Finally, the present study identifies the IoT-based entrepreneurial opportunities in the smart transportation industry and prioritizes them, providing practical insights regarding the creation of entrepreneurial ecosystems in the field of smart transportation for entrepreneurs and policymakers.

Noise abatement is one of the key techniques for Partial Discharge (PD) on-line measurement and monitoring. However, how to enhance the efficiency of PD signal noise suppression is a challenging work. Hence, this study aims to improve the efficiency of PD signal noise abatement.

In this approach, the time–frequency characteristics of PD signal had been obtained based on fast kurtogram and S-transform time–frequency spectrum, and these characteristics were used to optimize the parameters for the signal matching over-complete dictionary. Subsequently, a self-adaptive selection of matching atoms was realized when using Matching Pursuit (MP) to analyze PD signals, which leading to seldom noise signal element was represented in sparse decomposition.

The de-noising of PD signals was achieved efficiently. Simulation and experimental results show that the proposed method has good adaptability and significant noise abatement effect compared with Empirical Mode Decomposition, Wavelet Threshold and global signal sparse decomposition of MP.

A self-adaptive noise abatement method was proposed to improve the efficiency of PD signal noise suppression based on the signal sparse representation and its MP algorithm, which is significant to on-line PD measurement.

This study aims to examine the link between boards and audit committees and firm performance in Kuwaiti listed firms in the context of recent and extensive corporate governance regulatory reform.

Panel data regression analysis with fixed effects and clustered standard errors of firm performance for 61–97 listed industrial and services firms in Kuwait over a seven-year period. The dependent variables are the returns on assets and equity, the debt-to-equity ratio and leverage and Tobin’s Q and the independent variables comprise board of directors and audit committee characteristics, including size, the number of meetings and the numbers of independent and outside board and expert committee members. Firm size, subsidiary status and cash flow serve as control variables.

Mixed results with respect to the characteristics of the board of directors. Board size and independent and outsider board members positively relate only to Tobin’s Q and insiders only to debt to equity. For audit committee characteristics, committee size, independence and expertise positively relate to the return on equity and committee size and expertise only to Tobin’s Q. Of the five performance measures considered, board and audit committee characteristics together best determine Tobin’s Q.

Data from a single country limits generalisability and control variables necessarily limited in a developing market context. Need for qualitative insights into corporate governance reform as a complement to conventional quantitative analysis. In combining accounting and market information, Tobin’s Q appears best able to recognise the performance benefits of good corporate governance in terms of internal organisational change.

The recent corporate governance code and guidelines reforms exert a mixed impact on firm performance, with audit committees, not boards, of most influence. But recent reforms implied most change to boards of directors. One suggestion is that non-market reform may have been unneeded given existing market pressure on listed firms and firms anticipating regulatory change.

Kuwait’s corporate governance reforms codified corporate governance practices already in place among many of its firms in pursuit of organisational legitimacy, and while invoking substantial change to audit committees, involved minor change to firm performance, at least in the short term. Some firms may also have delisted in expectation of stronger corporate governance requirements. Regardless, these direct and indirect processes both improved the overall quality of listed firm corporate governance and performance in Kuwait.

Seminal analysis of corporate governance reforms in Kuwait, which have rapidly progressed from no corporate governance code and guidelines to an initially voluntary and then compulsory regime. Only known analysis to incorporate both board of directors and audit committee characteristics. Reveals studies of the corporate governance–firm performance relationship may face difficulty in model specification, and empirical significance, given the complexity of corporate governance codes and guidelines, leads in changing firm behaviour and self-selection of firms into and out of regulated markets.

The design phase is generally characterized with two-way multiple information exchanges/overlaps between the interdependent entities. In this paper, entity is a generic term to represent teams, components, activities or parameters. Existing approaches can either capture a single overlap or lack practical application in representing multiple overlaps. The beeline diagraming method (BDM) network is efficient in representing multiple overlaps for construction projects. However, it considers any entity as indivisible and cannot distinguish partial criticality of entities. In reality, the design phase in any construction project is driven by need basis and often has numerous interruptions. Hence, there is a need to develop an alternate network analysis for BDM for interruptible execution. The paper aims to discuss these issues.

A pilot study is conducted to formulate the hypothetical examples. Subsequently, these hypothetical BDM examples are analyzed to trace a pattern for criticality. This pattern study along with the existing precedence diagramming method network analysis enabled to derive new equations for forward pass, backward pass and float. Finally, the proposed concepts are applied to two design cases and reviewed with the design experts.

The proposed network analysis for BDM is efficient for interruptible entity execution.

The proposed BDM network is an information-intensive network that enables the design participants to view the project holistically. Application to two distinct cases emphasizes that the concept is generic and can be applied to any project that is characterized with beelines.

An alternate network analysis for BDM is investigated for interruptible entity execution. This study also clarifies the related concepts – interdependency, iteration, overlaps and multiple information exchanges/linkages.