A parking area shut during Covid is reopening to help offset the spaces lost during regeneration work.

Bedworth has marked 11 November on the actual day every year for more than 100 years.

New car market falls by -6.0% in October, as businesses, fleets and private buyers register 9,241 fewer vehicles. Battery electric the only vehicles to see higher uptake as manufacturers subsidise […]

The post EV deliveries rise in October as overall market shrinks appeared first on Tech Digest.

Last Friday (19th July 2019) an independent artist’s debut single was placed at the top of Spotify’s New Music Friday playlist and Apple Music’s Best of the Week playlist in the UK. Not only that but at the time of writing this, 5 days later the song is in a total of 54 Spotify editorial...

Peter Drucker, ‘the founder of modern management’ said:

Advanced marketing techniques such as Account-based Marketing (ABM) and 1-1 marketing require a more individualised approach than traditional inbound marketing tactics. No longer can we paint with a broad brush, as marketers. We must find ways to speak directly with individuals, rather than an audience.

Gifts, decorations and festive food.

Not an artisan sourdough loaf in sight.

Plans are put In the freezer, alongside the lamb chops.

When to go, what to see and how to save money.

We're adding support for return policies at the organization level, which means you'll be able to specify a general return policy for your business instead of having to define one for each individual product you sell.

Interactive video is increasingly becoming a more and more dominant feature of our media platforms. Especially due to the popular YouTube annotations framework, integrating graphical annotations in a video has become very fashionable these days. However, the current options are limited to a few graphical shapes for which the user can define as good as no dynamic behaviour. Despite the enormous demand for easy-creatable, interactive video there are no such advanced tools available.

In this article we describe an innovative approach, to realize dynamics and interactivity of video annotations. First we explain basic concepts of video-markup like the generic element model and visual descriptors. After that we introduce the event-tree model, which can be used to define event-handling in an interactive video formally as well as visually. By combining these basic concepts, we can give an effective tool to the video community for realizing interactive and dynamic video in a simple, intuitive and focused way.

1. McDavid closing in on 1,000 points, rare company at ‘remarkable’ pace  NHL.com
2. Draisaitl lifts in the OT winner  NHL.com
3. McDavid on brink of 1,000 points after huge performance in Oilers' OT win  Sportsnet.ca
4. McDavid's 4-point night leaves him one shy of 1,000-point milestone  TSN
5. Player grades: McDavid, Draisaitl to the rescue as Oilers pull another one out in overtime  Edmonton Journal

1. Niagara Health offering free parking after delays reported  News Talk 610 CKTB
2. Implementation of new Niagara Health patient info system resulting in long wait times  St. Catharines Standard
3. Temporary delays impacting registration at emergency departments  Thorold News
4. Niagara Health Working Through Delays  101.1 More FM
5. Niagara Health experiencing temporary delays impacting registration and EDs  Niagara Health

(Belga) L'ex-député européen Mark Demesmaeker, désormais sénateur coopté N-VA, sera la tête de liste du parti nationaliste pour les élections communales d'octobre 2024 à Hal (Brabant flamand), a-t-il indiqué lundi, confirmant des informations de presse.

M. Demesmaeker est conseiller communal à Hal depuis 2007 et y a été échevin durant six ans. La N-VA a terminé à deux reprises, lors des scrutins communaux de 2012 et 2018, comme premier parti de la commune. M. Demesmaeker a affirmé lundi toujours avoir cette ambition. "Nous avons une équipe jeune et soudée, avec expertise et vision. Je vise une progression", a-t-il dit. Il s'est dit convaincu que le changement de législation en Flandre - qui prévoit que le plus grand parti dispose d'un droit d'initiative pour former une coalition communale et que le candidat du plus grand groupe ayant recueilli le plus de voix de préférence devienne bourgmestre - lui offre un solide avantage. "Nous avons de bonnes chances, a-t-il souligné. L'élu nationaliste a affirmé vouloir se concentrer sur des thèmes comme l'urbanisation de Hal et la politique en matière de langue, en évoquant la "francisation de la ville". (Belga)

The current advertising push methods have low accuracy and poor advertising conversion effects. Therefore, a Weibo marketing advertising push method based on social network data mining is studied. Firstly, establish a social network graph and use graph clustering algorithm to mine the association relationships of users in the network. Secondly, through sparsisation processing, the association between nodes in the social network graph is excavated. Then, evaluate the tightness between user preferences and other nodes in the social network, and use the TF-IDF algorithm to extract user interest features. Finally, an attention mechanism is introduced to improve the deep learning model, which matches user interests with advertising domain features and outputs push results. The experimental results show that the push accuracy of this method is higher than 95%, with a maximum advertising click through rate of 82.7% and a maximum advertising conversion rate of 60.7%.

In order to solve the problems of long prediction consumption time and many prediction iterations existing in traditional prediction models, an e-commerce growth prediction model based on grey Markov chain is proposed. The Scrapy crawler framework is used to collect a variety of e-commerce data from e-commerce websites, and the feedforward neural network model is used to clean the collected data. With the cleaned e-commerce data as the input vector and the e-commerce growth prediction results as the output vector, an e-commerce growth prediction model based on the grey Markov chain is built. The prediction model is improved by using the background value optimisation method. After training the model through the improved particle swarm optimisation algorithm, accurate e-commerce growth prediction results are obtained. The experimental results show that the maximum time consumption of e-commerce growth prediction of this model is only 0.032, and the number of iterations is small.

In the present scenario, the security of medical images is an important aspect in the field of image processing. Support vector machines (SVMs) are a supervised machine learning technique used in image classification. The roots of SVM are from statistical learning theory. It has gained excellent significance because of its robust, accurate, and very effective algorithm, even though it was applied to a small set of training samples. SVM can classify data into binary classification or multiple classifications according to the application's needs. Discrete wavelet transform (DWT) and singular value decomposition (SVD) transform techniques are utilised to enhance the image's security. In this paper, the image is first classified using SVM into ROI and RONI, and thereafter, to enhance the images diagnostic capabilities, the DWT-SVD-based hybrid watermarking technique is utilised to embed the watermark in the RONI region. Overall, our work makes a significant contribution to the field of medical image security by presenting a novel and effective solution. The results are evaluated using both perceptual and imperceptibility testing using PSNR and SSIM parameters. Different attacks were introduced to the watermarked image, which shows the efficacy and robustness of the proposed algorithm.

Screen-shooting will lead to information leakage. Anti-screen-shooting watermark, which can track the leaking sources and protect the copyrights of images, plays an important role in image information security. Due to the randomness of shooting distance and angle, more robust watermark algorithms are needed to resist the mixed attack generated by screen-shooting. A robust digital watermarking algorithm that is resistant to screen-shooting is proposed in this paper. We use improved Harris-Laplace algorithm to detect the image feature points and embed the watermark into the feature domain. In this paper, all test images are selected on the dataset USC-SIPI and six related common algorithms are used for performance comparison. The experimental results show that within a certain range of shooting distance and angle, this algorithm presented can not only extract the watermark effectively but also ensure the most basic invisibility of watermark. Therefore, the algorithm has good robustness for anti-screen-shooting.

The video game industry (VGI) has evolved considerably, transitioning from a niche market to a substantial sector. The VGI's magnitude and the societal implications tied to video game consumption have naturally piqued the interest of scholars in marketing and consumer behaviour. This research serves a dual purpose: firstly, it consolidates existing VG literature by evaluating articles, concepts, and methodologies, systematically tracing their evolution; secondly, it outlines potential directions and implications for forthcoming research. Within this literature, a predominant focus lies on articles investigating purchase decisions concerning VGs, followed by those exploring the integration of video game consumption into broader social contexts. Notably, a limited number of articles delve into player-game interactions and experiences within gaming worlds. This imbalance can be attributed to the fact that such inquiries are often suited to psychology and multidisciplinary journals, while the marketing discipline has predominantly addressed the VGI from a marketing management standpoint.

Although artificial intelligence provides a new method to gather, process, analyse data, generate insights, and offer customised solutions, such methods could change how marketers deal with customers, and there is a lack of literature to portray the application of artificial intelligence in marketing. This study aims to recognise and portray the use of artificial intelligence from a marketing standpoint, as well as to provide a conceptual framework for the application of artificial intelligence in marketing. This study uses a systematic literature review analysis as a research method to achieve the aims. Data from 142 articles were extracted from the Scopus database using relevant search terms for artificial intelligence and marketing. The systematic review identified significant usage of artificial intelligence in conversational artificial intelligence, content creation, audience segmentation, predictive analytics, personalisation, paid ads, sales forecasting, dynamic pricing, and recommendation engines and the bibliometric analysis produced the trend in co-authorship, citation, bibliographic coupling, and co-citation analysis. Practitioners and academics may use this study to decide on the marketing area in which artificial intelligence can be invested and used.

This study investigates the effects of social media marketing efforts (SMME) on smartphone brand identification, attachment, association, and willingness to pay premium prices. A survey of 320 smartphone users who followed official social media handles managed by smartphone companies was conducted for this purpose. PLS-SEM was used to analyse the collected data. The findings demonstrated importance of SMME dimensions. According to the study's findings, the smartphone brand's SMMEs had significant impact on brand identification, brand association, and brand attachment. The results revealed that SMMEs had significant impact on willingness to pay the premium price. The findings also show that brand identification, attachment, and association mediated the relationship between SMMEs and willingness to pay a premium price. The findings of this study will be useful in developing social media marketing strategies for smartphones. This study demonstrates the use of social media marketing to promote mobile phones, particularly in emerging markets.

While digitalisation reforms aiming to enhance the quality of public services were put in place, most stakeholders in developing countries still use paper-based-tendering processes, which are associated with increased costs. To overcome these problems, calls to adopt e-bidding have recently emerged. This study aims to explore the readiness of Moroccan SMEs to adopt e-bidding. To achieve this goal, we proposed an integrated framework combining the TAM and UTAUT models to examine the predictors of SMEs' intention to adopt e-bidding. We empirically tested the conceptual model using a partial least squares (PLS) estimation based on data from 210 SMEs. Our results suggest that effort expectancy, facilitating conditions, and social influence as the key factors influencing SMEs intention to adopt e-bidding. We also suggest firm size as a significant moderator. This will help in improving SMEs' user experience and will also allow a better implementation of e-bidding in Morocco and similar contexts.

Since its birth, the stock market has received widespread attention from many scholars and investors. However, there are many factors that affect stock prices, including the company's own internal factors and the impact of external policies. The extent and manner of fundamental impacts also vary, making stock price predictions very difficult. Based on this, this article first introduces the research significance of the stock market prediction framework, and then conducts academic research and analysis on two key sentences of stock market prediction and artificial intelligence in stock market prediction. Then this article proposes a constructive algorithm theory, and finally conducts a simulation comparison experiment and summarises and discusses the experiment. Research results show that the neural network prediction method is more effective in stock market prediction; the minimum training rate is generally 0.9; the agency's expected dilution rate and the published stock market dilution rate are both around 6%.

In order to improve the standardisation of mutual information index, accuracy rate and recall rate of student classroom behaviour recognition method, this paper proposes a student's classroom behaviour recognition method based on abstract hidden Markov model (HMM). After cleaning the students' classroom behaviour data, improve the data quality through interpolation and standardisation, and then divide the types of students' classroom behaviour. Then, in support vector machine, abstract HMM is used to calculate the output probability density of support vector machine. Finally, according to the characteristic interval of classroom behaviour, we can judge the category of behaviour characteristics. The experiment shows that normalised mutual information (NMI) index of this method is closer to one, and the maximum AUC-PR index can reach 0.82, which shows that this method can identify students' classroom behaviour more effectively and reliably.

Because of the problems of low mining precision and slow mining speed in traditional enterprise marketing investment data mining methods, a fast mining method for enterprise marketing investment databased on improved association rules is proposed. First, the enterprise marketing investment data is collected through the crawler framework, and then the collected data is cleaned. Then, the cleaned data features are extracted, and the correlation degree between features is calculated. Finally, according to the calculation results, all data items are used as constraints to reduce the number of frequent itemsets. A pruning strategy is designed in advance. Combined with the constraints, the Apriori algorithm of association rules is improved, and the improved algorithm is used to calculate all frequent itemsets, Obtain fast mining results of enterprise marketing investment data. The experimental results show that the proposed method is fast and accurate in data mining of enterprise marketing investment.

In order to solve the problems of large generalisation error, low recall rate and low retrieval accuracy of customer evaluation information in traditional trust evaluation methods, an evaluation method of customer trust in e-commerce market based on entropy weight analytic hierarchy process was designed. Firstly, build an evaluation index system of customer trust in e-commerce market. Secondly, the customer trust matrix is established, and the index weight is calculated by using the analytic hierarchy process and entropy weight method. Finally, five-scale Likert method is used to analyse the indicator factors and establish a comment set, and the trust evaluation value is obtained by combining the indicator membership. The experiment shows that the maximum generalisation error of this method is only 0.029, the recall rate is 97.5%, and the retrieval accuracy of customer evaluation information is closer to 1.

In order to overcome the problems of low efficiency, low user satisfaction and poor customer growth rate under the traditional marketing strategy, this paper studies the innovative strategy of mobile payment business marketing strategy under the internet environment. First of all, study the status quo of mobile payment business marketing in the internet environment, obtain mobile payment business data through questionnaire survey, and analyse the problems in mobile payment business marketing. Secondly, build a user profile of mobile payment business marketing, and classify user attributes, consumption characteristics and user activity through K-means clustering method; Finally, the marketing strategy is innovated from three aspects: product marketing, pricing marketing and channel marketing. The results show that the marketing benefit after the application of this strategy is 19.52 million yuan, the user satisfaction can reach 98.9%, and the customer growth rate can reach 21.3%, improving the marketing benefit of mobile payment business.

This research delves into the wealth effect of shareholders from bidding firms created by mergers and acquisitions (M&A) in China and India, two of the world's most populous nations. The study reveals that on average, M&A deals create wealth for shareholders of the acquiring firms, as determined by abnormal percentage returns in a five-day event window. Regarding the further classification of acquiring firms based on industry, the abnormal percentage returns vary in different sectors in both countries. In China, shareholders benefit in seven out of ten industries, while in India, they gain in five out of nine industries. Moreover, the stock markets' responses vary depending on the type of M&A in each country. Cross-industry M&A deals in China generate higher gains for shareholders than within-industry deals, whereas, in India, within-industry M&A deals generate higher gains.

Aim/Purpose: Despite millions of apps on the market, it is still challenging to develop a mobile app that can run across platforms using the same code. Background: This paper explores a potential solution for developing cross platform apps by presenting the hybrid app approach. Methodology: The paper first describes a brief evolution of the different mobile app development approaches and then compares them with the hybrid app approach. Next, it focuses on one specific hybrid app development framework called Ionic. Contribution: The paper presents the hybrid app approach as an emerging trend in mobile app development and concludes with the highlight of its advantages and teaching implications. Findings: The hybrid app approach reduces the learning curve and offers tools to allow the reuse of code to create apps for different mobile devices. Recommendations for Practitioners: The experience that the paper describes in using Ionic framework to create a hybrid app can be adopted in a web design or mobile app development course. Impact on Society : The advance in hybrid framework in general and the growing acceptance of open source framework, such as Ionic in particular, may provide an alternative to the native app domination and may trigger the rapid rise of hybrid apps in the years to come.

Aim/Purpose: Executive doctoral programs in higher education are under-researched. Scholars, administers, and students should be aware of all common delivery methods for higher education graduate programs. Background This paper provides a review and analysis of executive doctoral higher education programs in the United States. Methodology: Executive higher education doctoral programs analyzed utilizing a qualitative demographic market-based analysis approach. Contribution: This review of executive higher education doctoral programs provides one of the first investigations of this segment of the higher education degree market. Findings: There are twelve programs in the United States offering executive higher education degrees, though there are less aggressively marketed programs described as executive-style higher education doctoral programs that could serve students with similar needs. Recommendations for Practitioners: Successful executive higher education doctoral programs require faculty that have both theoretical knowledge and practical experience in higher education. As appropriate, these programs should include tenure-line, clinical-track, and adjunct faculty who have cabinet level experience in higher education. Recommendation for Researchers: Researchers should begin to investigate more closely the small but growing population of executive doctoral degree programs in higher education. Impact on Society: Institutions willing to offer executive degrees in higher education will provide training specifically for those faculty who are one step from an executive position within the higher education sector. Society will be impacted by having someone that is trained in the area who also has real world experience. Future Research: Case studies of students enrolled in executive higher education programs and research documenting university-employer goals for these programs would enhance our understanding of this branch of the higher education degree market.

Aim/Purpose. The most crucial aspects of teaching a foreign language to more advanced learners are building an awareness of discourse modes, how to regulate discourse, and the pragmatic properties of discourse components. However, in different languages, the connections and structure of discourse are ensured by different linguistic means which makes matters complicated for the learner. Background. By uncovering regularities in a foreign language and comparing them with patterns in one’s own tongue, the corpus research method offers the student unique opportunities to acquire linguistic knowledge about discourse markers. This paper reports on an investigation of the functions of multi-word discourse markers. Methodology. In our research, we combine the alignment model of the phrase-based statistical machine translation and manual treatment of the data in order to examine English multi-word discourse markers and their equivalents in Lithuanian and Hebrew translations by researching their changes in translation. After establishing the full list of multi-word discourse markers in our generated parallel corpus, we research how the multi-word discourse markers are treated in translation. Contribution. Creating a parallel research corpus to identify multi-word expressions used as discourse markers, analyzing how they are translated into Lithuanian and Hebrew, and attempting to determine why the translators made the choices add value to corpus-driven research and how to manage discourse. Findings. Our research proves that there is a possible context-based influence guiding the translation to choose a particle or other lexical item integration in Lithuanian or Hebrew translated discourse markers to express the rhetorical domain which could be related to the so-called phenomenon of “over-specification.” Recommendations for Practitioners. The comparative examination of discourse markers provides language instructors and translators with more specific information about the roles of discourse markers. Recommendations for Researchers. Understanding the multifunctionality of discourse markers provides new avenues for discourse marker application in translation research. Impact on Society. The current study may be a useful method to strengthen students’ language awareness and analytic skills and is particularly important for students specializing in English philology or translation. Beyond the empirical research, an extensive parallel data resource has been created to be openly used. Future Research. It should be noted that the observed phenomenon of “over-specification” could be analyzed further in future research.

Aim/Purpose: Two research objectives were addressed in this study. The first objective was to determine the effect of knowledge management orientation behaviour on business performance, and the second objective was to investigate the mediating effect of market orientation in the relationship between knowledge management orientation behaviour and business performance. Background: In business strategic perspective, the idea of knowledge management has been discussed widely. However, there is a lack of study exploring the notion of knowledge management orientation especially in the perspective of Indonesia’s creative economy sector. Methodology: One hundred and thirty one participants were involved in this study. They were economy creative practitioners in Indonesia. Data were analysed by using Partial Least Squares. Contribution: Upon the completion of the research objectives, this study contributes to both theoretical and practical perspectives. From a theoretical standpoint, this study proposes a conceptual model explaining the relationship among knowledge management orientation behaviour, market orientation, and business performance in Indonesia’s creative economy sector. As this study found a significant effect of knowledge sharing in market orientation and market orientation in business performance, the study showed the mediation role of market orientation in the relationship between knowledge sharing and business performance. From a practical perspective, this study implies a guideline for business practitioners in enhancing business through the application of knowledge management orientation behaviour. Findings: The results show that organizing memory, knowledge absorption, and knowledge receptivity has a direct significant effect on business performance. However, in affecting business performance, knowledge sharing must be mediated by market orientation. Recommendations for Practitioners: Based on the results of the study, practitioners should enhance their behaviour in implementing knowledge management in terms of increasing business performance. In addition, it is suggested that business practitioners must be market driven, as market orientation was found to have an important role in affecting business performance. Recommendation for Researchers: Future researchers might integrate other constructs such as innovation, marketing capabilities, or organizational learning with this current conceptual model to have more comprehensive insight about the relationship between knowledge management orientation and business performance. Impact on Society: This study suggests that business practitioners must have knowledge management driven behaviour as well as market orientation to enhance the performance of their business. Future Research: Future research might add other variables to make the conceptual model more comprehensive and also replicate this study into different industrial settings.

Aim/Purpose: This study aimed to examine the effect of marketing knowledge management (MKM) on bank performance via the mediating role of the Fintech innovation in Jordanian commercial banks. Background: An extensive number of studies found a significant relationship between Marketing knowledge management and bank performance (e.g., Akroush & Al-Mohammad, 2010; Hou & Chien 2010; Rezaee & Jafari, 2015; Veismoradi et al., 2013). However, there remains a lack of clarity regarding the relationship between marketing knowledge management (MKM) and bank performance (BP). Furthermore, the linkage between MKM and BP is not straightforward but, instead, includes a more complicated relationship. Therefore, it is argued that managing marketing knowledge management assets and capabilities can enhance performance via the role of financial innovation as a mediating factor on commercial banks; to date, however, there is no empirical evidence. Methodology: Based on a literature review, knowledge-based theory, and financial innovation theory, an integrated conceptual framework has been developed to guide the study. A quantitative approach was used, and the data was collected from 336 managers and employees in all 13 Jordanian commercial banks using online and in hand instruments. Structural equation modeling (SEM) was used to analyze and verify the study variables. Contribution: This article contributes to theory by filling a gap in the literature regarding the role of marketing knowledge management assets and capabilities in commercial banks operating in a developing country like Jordan. It empirically examined and validated the role of Fintech innovation as mediators between marketing knowledge management and bank performance Findings: The main findings revealed that marketing knowledge management had a significant favorable influence on bank performance. Fintech innovation acted as partial mediators in this relationship. Recommendations for Practitioners: Commercial banks should be fully aware of the importance of knowledge management practices to enhance their financial innovation and bank performance. They should also consider promoting a culture of practicing knowledge management processes among their managers and employees by motivating and training to promote innovations. Recommendation for Researchers: The result endorsed Fintech innovation’s mediating effect on the relationship between the independent variable, marketing knowledge management (assets and capabilities), and the dependent variable bank performance, which was not addressed before; thus, it needs further validation. Future Research: The current designed research model can be applied and assessed further in other sectors, including banking and industrial sectors across developed and developing countries. It would also be of interest to introduce other variables in the study model that can act as consequences of MKM capabilities, such as financial and non-financial performance measures

Aim/Purpose: The study aims to explore the attributes of self-phubbing and partner-phubbing, as well as their impact on marital relationship satisfaction and the quality of communication. Furthermore, it aims to comprehend how these characteristics could impact an individual’s total level of life satisfaction. Background: The study aims to establish a clear association between specific mobile phone usage behaviors and their subsequent impact on relationship satisfaction and the quality of communication. This study investigates the effects of two types of behaviors on interpersonal relationships: self-phubbing, which refers to an individual being deeply absorbed in their own mobile phone use, and partner-phubbing, which refers to witnessing one’s partner being deeply absorbed in a mobile device. Methodology: This study utilizes a quantitative approach. The poll involved 150 smartphone users in Malaysia who are in relationships, and they participated by completing a questionnaire. The data analysis was performed using the Partial Least Squares-based Structural Equation Modeling method. Contribution: This research addresses the gap and gives insight into the consequences of self and partner phubbing and its impact on the relationship and life satisfaction among partners by providing a research model that was validated with primary data. Findings: The results of this survey show that smartphone conflicts harm relationship satisfaction but not communication quality. It was revealed that communication quality does not directly bring a negative impact on life satisfaction, but it directly affects relationship satisfaction, which, in turn, harms life satisfaction. Recommendations for Practitioners: The findings of this study can be used by practitioners to improve relationship counseling and therapy. Through the integration of the notion of phubbing and its impact on relationship happiness, couples can receive guidance on how to reduce the tension that arises from using smartphones. Recommendation for Researchers: Previous research was conducted exclusively on only an individual’s phubbing behavior, but limited work was done on the partner’s phubbing behavior. Future researchers can enhance this model by identifying more factors. Impact on Society: This study addresses broader societal ramifications in addition to the dynamics of particular relationships. This study promotes a more mindful use of smartphones by exposing the complex relationships between technology use, relationship happiness, and general life contentment. This will ultimately lead to healthier relationships and improved societal well-being. Future Research: In the future, we are going to implement an artificial neural network approach to test this data to predict the most important factors that influence phubbing.

Aim/Purpose: Given the increasingly important role of knowledge and human resources for firms in developing and emerging countries to pursue innovation, this paper aims to study and explore the potential intermediating roles of knowledge donation and collection in linking high-involvement human resource management (HRM) practice and innovation capability. The paper also explores possible moderators of market turbulence in fostering the influences of knowledge-sharing (KS) behaviors on innovation competence in terms of incremental and radical innovation. Background: The fitness of HRM practice is critical for organizations to foster knowledge capital and internal resources for improving innovation and sustaining competitive advantage. Methodology: The study sample is 309 respondents and Structural Equation Model (SEM) was used for the analysis of the data obtained through a questionnaire survey with the aid of AMOS version 22. Contribution: This paper increases the understanding of the precursor role of high-involvement HRM practices, intermediating mechanism of KS activities, and the regulating influence of market turbulence in predicting and fostering innovation capability, thereby pushing forward the theory of HRM and innovation management. Findings: The empirical findings support the proposed hypotheses relating to the intermediating role of KS in the HRM practices-innovation relationship. It spotlights the crucial character of market turbulence in driving the domination of knowledge-sharing behaviors on incremental innovation. Recommendations for Practitioners: The proposed research model can be applied by leaders and directors to foster their organizational innovation competence. Recommendation for Researchers: Researchers are recommended to explore the influence of different models of HRM practices on innovation to identify the most effective pathway leading to innovation for firms in developing and emerging nations. Impact on Society: This paper provides valuable initiatives for firms in developing and emerging markets on how to leverage the strategic and internal resources of an organization for enhancing innovation. Future Research: Future studies should investigate the influence of HRM practices and knowledge resources to promote frugal innovation models for dealing with resource scarcity.

Aim/Purpose: This study aims to examine the ten factors from the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Unified Theory of Acceptance and Use of Technology (UTAUT) theories in order to analyze behavioral intentions to use the Indonesian online capital market investment platforms and the effect of behavioral intentions on actual usage. Background: The potential growth of capital market investors in Indonesia is large, and the low use of the Internet for investment purposes makes it necessary for stakeholders to understand the factors that affect people’s intentions to invest, especially through online platforms. Several previous studies have explained the intention to use online investment platforms using the TAM and TPB theories. This study tries to combine TAM, TPB, and UTAUT theories in analyzing behavioral intentions to use an online capital market investment platform in Indonesia. Methodology: The research approach employed is a mixed method, particularly explanatory research, which employs quantitative methods first, followed by qualitative methods. Data were collected by conducting interviews and sending online surveys. This study was successful in collecting information on the users of online capital market investment platforms in Indonesia from 1074 respondents, which was then processed and analyzed using Covariance-Based Structural Equation Modeling (CB-SEM) with the IBM AMOS 26.0 application. Contribution: This study complements earlier theories like TAM, TPB, and UTAUT by looking at the intention to use online capital market investment platforms from technological, human, and environmental viewpoints. This study looks at the intention to use the online capital market investing platform as a whole rather than separately depending on investment instruments. This study also assists practitioners including regulators, the government, developers, and investors by offering knowledge of the phenomena and factors that can increase the capital market’s investment intention in Indonesia. Findings: Attitudes, perceived ease of use, perceived behavioral control, subjective norm, and national pride were found to be significant predictors of the intention to use online investment platforms in Indonesia, whereas perceived usefulness, perceived risk, perceived trust, perceived privacy, and price value were not. Recommendations for Practitioners: All practitioners must be able to take steps and strategies that focus on factors that have a significant impact on increasing usage intentions. The government can enact legislation that emphasizes the simplicity and convenience of investment, as well as launch campaigns that encourage people to participate in economic recovery by investing in the capital market. Meanwhile, the developers are concentrating on facilitating the flow of investment transactions through the platform, increasing education and awareness of the benefits of investing in the capital market, and providing content that raises awareness that investing in the capital market can help to restore the national economy. Recommendation for Researchers: Further research is intended to include other variables such as perceived benefits and perceived security, as well as other frameworks such as TRA, to better explain individuals’ behavioral intentions to use online capital investment platforms. Impact on Society: This study can help all stakeholders understand what factors can increase Indonesians’ interest in investing in the capital market, particularly through online investment platforms. This understanding is expected to increase the number of capital market participants and, as a result, have an impact on economic recovery following the COVID-19 pandemic. Future Research: Future research is expected to investigate additional factors that can influence individuals’ behavioral intention to use an online capital market investment platform, such as perceived benefits and perceived security, as well as the addition of control variables such as age, gender, education, and income. International research across nations is also required to build a larger sample size in order to examine the behavior of investors in developing and developed countries and acquire a more thorough understanding of the online capital market investment platform.

Aim/Purpose: Accurate detection of vacant parking spaces is crucial for autonomous parking. Deep learning, particularly Graph Neural Networks (GNNs), holds promise for addressing the challenges of diverse parking lot appearances and complex visual environments. Our GNN-based approach leverages the spatial layout of detected marking points in around-view images to learn robust feature representations that are resilient to occlusions and lighting variations. We demonstrate significant accuracy improvements on benchmark datasets compared to existing methods, showcasing the effectiveness of our GNN-based solution. Further research is needed to explore the scalability and generalizability of this approach in real-world scenarios and to consider the potential ethical implications of autonomous parking technologies. Background: GNNs offer a number of advantages over traditional parking spot detection methods. Unlike methods that treat objects as discrete entities, GNNs may leverage the inherent connections among parking markers (lines, dots) inside an image. This ability to exploit spatial connections leads to more accurate parking space detection, even in challenging scenarios with shifting illumination. Real-time applications are another area where GNNs exhibit promise, which is critical for autonomous vehicles. Their ability to intuitively understand linkages across marking sites may further simplify the process compared to traditional deep-learning approaches that need complex feature development. Furthermore, the proposed GNN model streamlines parking space recognition by potentially combining slot inference and marking point recognition in a single step. All things considered, GNNs present a viable method for obtaining stronger and more precise parking slot recognition, opening the door for autonomous car self-parking technology developments. Methodology: The proposed research introduces a novel, end-to-end trainable method for parking slot detection using bird’s-eye images and GNNs. The approach involves a two-stage process. First, a marking-point detector network is employed to identify potential parking markers, extracting features such as confidence scores and positions. After refining these detections, a marking-point encoder network extracts and embeds location and appearance information. The enhanced data is then loaded into a fully linked network, with each node representing a marker. An attentional GNN is then utilized to leverage the spatial relationships between neighbors, allowing for selective information aggregation and capturing intricate interactions. Finally, a dedicated entrance line discriminator network, trained on GNN outputs, classifies pairs of markers as potential entry lines based on learned node attributes. This multi-stage approach, evaluated on benchmark datasets, aims to achieve robust and accurate parking slot detection even in diverse and challenging environments. Contribution: The present study makes a significant contribution to the parking slot detection domain by introducing an attentional GNN-based approach that capitalizes on the spatial relationships between marking points for enhanced robustness. Additionally, the paper offers a fully trainable end-to-end model that eliminates the need for manual post-processing, thereby streamlining the process. Furthermore, the study reduces training costs by dispensing with the need for detailed annotations of marking point properties, thereby making it more accessible and cost-effective. Findings: The goal of this research is to present a unique approach to parking space recognition using GNNs and bird’s-eye photos. The study’s findings demonstrated significant improvements over earlier algorithms, with accuracy on par with the state-of-the-art DMPR-PS method. Moreover, the suggested method provides a fully trainable solution with less reliance on manually specified rules and more economical training needs. One crucial component of this approach is the GNN’s performance. By making use of the spatial correlations between marking locations, the GNN delivers greater accuracy and recall than a completely linked baseline. The GNN successfully learns discriminative features by separating paired marking points (creating parking spots) from unpaired ones, according to further analysis using cosine similarity. There are restrictions, though, especially where there are unclear markings. Successful parking slot identification in various circumstances proves the recommended method’s usefulness, with occasional failures in poor visibility conditions. Future work addresses these limitations and explores adapting the model to different image formats (e.g., side-view) and scenarios without relying on prior entry line information. An ablation study is conducted to investigate the impact of different backbone architectures on image feature extraction. The results reveal that VGG16 is optimal for balancing accuracy and real-time processing requirements. Recommendations for Practitioners: Developers of parking systems are encouraged to incorporate GNN-based techniques into their autonomous parking systems, as these methods exhibit enhanced accuracy and robustness when handling a wide range of parking scenarios. Furthermore, attention mechanisms within deep learning models can provide significant advantages for tasks that involve spatial relationships and contextual information in other vision-based applications. Recommendation for Researchers: Further research is necessary to assess the effectiveness of GNN-based methods in real-world situations. To obtain accurate results, it is important to employ large-scale datasets that include diverse lighting conditions, parking layouts, and vehicle types. Incorporating semantic information such as parking signs and lane markings into GNN models can enhance their ability to interpret and understand context. Moreover, it is crucial to address ethical concerns, including privacy, potential biases, and responsible deployment, in the development of autonomous parking technologies. Impact on Society: Optimized utilization of parking spaces can help cities manage parking resources efficiently, thereby reducing traffic congestion and fuel consumption. Automating parking processes can also enhance accessibility and provide safer and more convenient parking experiences, especially for individuals with disabilities. The development of dependable parking capabilities for autonomous vehicles can also contribute to smoother traffic flow, potentially reducing accidents and positively impacting society. Future Research: Developing and optimizing graph neural network-based models for real-time deployment in autonomous vehicles with limited resources is a critical objective. Investigating the integration of GNNs with other deep learning techniques for multi-modal parking slot detection, radar, and other sensors is essential for enhancing the understanding of the environment. Lastly, it is crucial to develop explainable AI methods to elucidate the decision-making processes of GNN models in parking slot detection, ensuring fairness, transparency, and responsible utilization of this technology.