Search results for: marketing intelligence

Authors: Saad Saif

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Tourism promotion in the digital age is significantly influenced by social media, particularly in developing travel markets such as Pakistan. This study examines how travel bloggers use social media to inspire people to plan journeys and increase trust in destinations. It examines how trustworthiness works as a moderator to enhance the legitimacy of social media posts. This study aims to comprehend the dynamics of social media's influence on the travel and tourism industry. This study investigates the influence of travel bloggers' content, with a focus on tone (positive/negative) and emotional intensity (strong/weak), on prospective Pakistani travelers' travel preferences and levels of trust toward a particular location. The study used an experimental design to validate its hypotheses. The results indicate that the emotive content and tone of bloggers influence travel intentions and that destination trust mediates this relationship. It is interesting to observe that variations in the emotional intensity of positive and negative ratings are not always accompanied by changes in destination trust and travel intent. In addition, the influence of a blogger's review tone on travel intention and destination trust is moderated by the credibility of online reviews, whereas the influence of emotional intensity on these outcomes is unaffected by review credibility.

Keywords: tourism marketing, destination trust, travel intention, trustworthiness

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Authors: L. K. Davis

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The effects of the evolution force are observable in nature at all structural levels ranging from small molecular systems to conversely enormous biospheric systems. However, the evolution force and work associated with formation of biological structures has yet to be described mathematically or theoretically. In addressing the conundrum, we consider evolution from a unique perspective and in doing so we introduce the “Fundamental Theory of the Evolution Force: FTEF”. We utilized synthetic evolution artificial intelligence (SYN-AI) to identify genomic building blocks and to engineer 14-3-3 ζ docking proteins by transforming gene sequences into time-based DNA codes derived from protein hierarchical structural levels. The aforementioned served as templates for random DNA hybridizations and genetic assembly. The application of hierarchical DNA codes allowed us to fast forward evolution, while dampening the effect of point mutations. Natural selection was performed at each hierarchical structural level and mutations screened using Blosum 80 mutation frequency-based algorithms. Notably, SYN-AI engineered a set of three architecturally conserved docking proteins that retained motion and vibrational dynamics of native Bos taurus 14-3-3 ζ.

Keywords: 14-3-3 docking genes, synthetic protein design, time-based DNA codes, writing DNA code from scratch

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Authors: Adedeji S. Adegoke, Olakunle N. Popoola

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Promotion is indispensible and inestimable property of marketing through which different organizations persuade their prospective customers. The idea of passing information about a product to the consumer at outside the world is known as promotional activities. A study was determined whether there was relationship between promotional mix and consumer buying decision, that is may be customers were influenced by promotion. It was investigated to determine whether promotion can be used to influence competitors’ activities in the market and also research was conducted to determine if there was any problem encountered by Nigeria bottling company plc, in promoting its beverages products. The various forms of promotional mix available for an organization were examined and recommended the appropriate promotional mix that company can adopt to boost the company sales. The research design was depended on the primary and secondary data. The primary data were information collected from the subjects using methods of data collection, that is through the use of questionnaire, interview, direct observation, etc. The secondary data consist of information that already exists having been collected for another purpose by some researchers. These include internal and external sources. The questionnaire was designed and administered to the staff of production and marketing department of Nigeria bottling company plc., which served as the population of this study, out of which sample was drawn randomly from the population, using sample random technique. It was deduced that 90% of the respondents opined that advertising influenced competition in the market and that there was a good sale after they started advert while 10% of them were not sure. At advertising level, 85% of the respondents chose 81-100% as the increase in the percentage recorded in their sales level, while 10% of them agreed that increase in the percentage recorded in their sales was within 61-80% and 5% of them chose 45-60% as the percentage increase in their sales record. Due to unstable economic condition of the Nigeria, many business organizations adopted the promotional strategies. Apart from advertising, it was discovered through research that sales promotion served as an incentive to consumers of Nigeria bottling company plc at a time offer gifts and prizes to consumers which drastically increased their level of sales. Since advertising and sales promotion increased the level of sales, more money should be allocated for this purpose to maintain market share and thereby increase profit.

Keywords: consumer, marketing, organization, promotional mix

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Authors: Kanamon Suwantada

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The purpose of this research is to understand customer’s expectations towards homestays and to establish the precise strategies to increase numbers of tourists for homestay business in Amphawa district, Samutsongkram, Thailand. The researcher aims to ensure that each host provides experiences to travelers who are looking for and determining new targets for homestay business in Amphawa as well as creating sustainable homestay using marketing strategies to increase customers. The methods allow interview and questionnaire to gain both overview data from the tourists and qualitative data from the homestay owner’s perspective to create a GAP analysis. The data was collected from 200 tourists, during 15th May - 30th July, 2011 from homestay in Amphawa Community. The questionnaires were divided into three sections: the demographic profile, customer information and influencing on purchasing position, and customer expectation towards homestay. The analysis, in fact, will be divided into two methods which are percentage and correlation analyses. The result of this research revealed that homestay had already provided customers with reasonable prices in good locations. Antithetically, activities that they offered still could not have met the customer’s requirements. Homestay providers should prepare additional activities such as village tour, local attraction tour, village daily life experiences, local ceremony participation, and interactive conversation with local people. Moreover, the results indicated that a price was the most important factor for choosing homestay.

Keywords: ecotourism, homestay, marketing, sufficiency economic philosophy

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Authors: Steffon Campbell

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This research explores the potential of using artificial intelligence (AI) autoethnographies to study, document, explore, and understand aspects of Caribbean culture. As a digital research methodology, AI autoethnography merges computer science and technology with ethnography, providing a fresh approach to collecting and analyzing data to generate novel insights. This research investigates how AI autoethnography can best be applied to understanding the various complexities and nuances of Caribbean culture, as well as examining how technology can be a valuable tool for enriching study of the region. By applying AI autoethnography to Caribbean studies, the research aims to produce new and innovative ways of discovering, understanding, and appreciating the Caribbean. The study found that AI autoethnographies can offer a valuable method for exploring Caribbean culture. Specifically, AI autoethnographies can facilitate experiences of self-reflection, facilitate reconciliation with the past, and provide a platform to explore and understand the cultural, social, political, and economic concerns of Caribbean people. Findings also reveal that these autoethnographies can create a space for people to reimagine and reframe the conversation around Caribbean culture by enabling them to actively participate in the process of knowledge creation. The study also finds that AI autoethnography offers the potential for cross-cultural dialogue, allowing participants to connect with one another over cultural considerations and engage in meaningful discourse.

Keywords: artificial intelligence, autoethnography, caribbean, culture

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Authors: Laura Struik, Sarah Dow-Fleisner, Robert Janke

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Introduction: The use of e-cigarettes, also known as vaping, has risen exponentially among North American youth and young adults (YYA) in recent years and has become a critical public health concern. The marketing strategies used by e-cigarette companies have been associated with the uptick in use among YYA, with video advertisements on TV and other electronic platforms being the most pervasive strategy. It is unknown if or how these advertisements capitalize on the recently documented multi-faceted influences that contribute to the initiation of vaping among this demographic (e.g., stress, anxiety, gender, peers, etc.), which is examined in this study. Methods: This content analysis is phase one of a two-phased research project that aims to inform meaningful approaches to anti-vaping messaging and campaigns. As part of this first phase, a scoping review has been conducted to identify various influences (environmental, cognitive, contextual, social, and emotional) on e-cigarette uptake among YYA. The results of this scoping review will inform the development of a coding framework to analyze the multiple influences present in vaping advertisements, as seen on two popular television channels (Discovery and AMC). In addition, advertisement characteristics will be incorporated into the coding framework (e.g., the number of people present, demographic details, context, and setting, etc.), and analyzed. Findings: Findings will reveal the types of influences being leveraged in vaping advertisements, and identify the underlying messages that may be particularly attractive to YYA. This will contribute to a more nuanced understanding of how e-cigarette companies market their products and to whom. The results will also inform the next phase of this research project, which will encompass an analysis of anti-vaping advertisements and how the underpinning strategies align with those of the pro-vaping advertisements. Conclusions: Findings of this will study bring forward important implications for developing effective anti-vaping messages, and assist public health professionals in providing more comprehensive prevention and cessation support as it relates to e-cigarette use. Understanding which marketing strategies e-cigarette companies use is vital to our understanding of how to combat them. Findings will inform recommendations for public health efforts aimed at curbing e-cigarette use among YYA, and ultimately contribute to the health and well-being of YYA.

Keywords: e-cigarettes, youth and young adults, advertisements, public health

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Authors: Nawal Alawad, Passent Ibrahim Tantawi, Mohamed Abdel Salam Ragheb

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Providing customers with quality experiences has been confirmed to be a sustainable, competitive advantage with a distinct financial impact for companies. The success of service providers now relies on their ability to provide customer-centric services. The importance of perceived service quality and customer experience is widely recognized. The focus of this research is in the area of mobile and fixed broadband services. This study is of dual importance both academically and practically. Academically, this research applies a new model investigating the impact of customer experience quality on loyalty based on modifying the multiple-item scale for measuring customers’ service experience in a new area and did not depend on the traditional models. The integrated scale embraces four dimensions: service experience, outcome focus, moments of truth and peace of mind. In addition, it gives a scientific explanation for this relationship so this research fill the gap in such relations in which no one correlate or give explanations for these relations before using such integrated model and this is the first time to apply such modified and integrated new model in telecom field. Practically, this research gives insights to marketers and practitioners to improve customer loyalty through evolving the experience quality of broadband customers which is interpreted to suggested outcomes: purchase, commitment, repeat purchase and word-of-mouth, this approach is one of the emerging topics in service marketing. Data were collected through 412 questionnaires and analyzed by using structural equation modeling.Findings revealed that both outcome focus and moments of truth have a significant impact on loyalty while both service experience and peace of mind have insignificant impact on loyalty.In addition, it was found that 72% of the variation occurring in loyalty is explained by the model. The researcher also measured the net prompters score and gave explanation for the results. Furthermore, assessed customer’s priorities of broadband services. The researcher recommends that the findings of this research will extend to be considered in the future plans of Telecom Egypt Group. In addition, to be applied in the same industry especially in the developing countries that have the same circumstances with similar service settings. This research is a positive contribution in service marketing, particularly in telecom industry for making marketing more reliable as managers can relate investments in service experience directly with the performance closest to income for instance, repurchasing behavior, positive word of mouth and, commitment. Finally, the researcher recommends that future studies should consider this model to explain significant marketing outcomes such as share of wallet and ultimately profitability.

Keywords: broadband services, customer experience quality, loyalty, net promoters score

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Authors: Nedjar Abdelhadi

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The pharmaceutical industry is one of the most important structures and foundations for the management and development of the modern world, especially the advanced part of it, meaning that there are some exceptions for third-world countries. The world today has witnessed radical transformations and changes, some of which made it better and some of which affected the path of its growth. At the beginning of my research, there was a detailed presentation overview of the current situation of the world in terms of growth and development, and it proceeded through that overview as the introduction to my research. The first chapter had divided into three sections; each topic was unique to one of the new methods of manufacturing, deducing and developing medicines Several examples of various recently developed medicines were used The second chapter dealt with the defects and shortcomings that pioneers and drug makers at various levels, as well as various regions and major companies, suffer from on the basis that they are international, especially those specialized in the manufacture of medicines related to viruses and chronic diseases, as well as incurable. As for the third chapter, it was devoted to marketing methods, methods of achieving sales, as well as the basics of spreading medicines and preparing the minds of consumers. Through my research, the one concluded that the current world has become completely different from the world we used to know, and it means by saying the field of manufacturing, selling and marketing medicines. It was noted that one of the biggest factors that affected the change in the field of medicine was the corona disaster. At the end of my research, I was left with nothing but to show the importance and necessity of the pharmaceutical industry and its effective role, not only in the development of mankind, but its main role is in the survival of mankind.

Keywords: health, diseases, medicine, epidemics

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Authors: Emine Akar

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The majority of privacy-related regulation has traditionally focused on concepts that are perceived to be well-understood or easily describable, such as certain categories of data and personal information or images. In the past century, such regulation appeared reasonably suitable for its purposes. However, technologies such as AI, combined with ever-increasing capabilities to collect, process, and store “big data”, not only require calibration of these traditional understandings but may require re-thinking of entire categories of privacy law. In the presentation, it will be explained, against the background of various emerging technologies under the umbrella term “emotional artificial intelligence”, why modern privacy law will need to embrace human emotions as potentially private subject matter. This argument can be made on a jurisprudential level, given that human emotions can plausibly be accommodated within the various concepts that are traditionally regarded as the underlying foundation of privacy protection, such as, for example, dignity, autonomy, and liberal values. However, the practical reasons for regarding human emotions as potentially private subject matter are perhaps more important (and very likely more convincing from the perspective of regulators). In that respect, it should be regarded as alarming that, according to most projections, the usefulness of emotional data to governments and, particularly, private companies will not only lead to radically increased processing and analysing of such data but, concerningly, to an exponential growth in the collection of such data. In light of this, it is also necessity to discuss options for how regulators could address this emerging threat.

Keywords: AI, privacy law, data protection, big data

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Authors: Dong Seop Lee, Byung Sik Kim

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In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. These artificial intelligence technologies are applied in various services, including disaster management. Disaster information management does not just support disaster work, but it is also the foundation of smart disaster management. Furthermore, it gets historical disaster information using artificial intelligence technology. Disaster information is one of important elements of entire disaster cycle. Disaster information management refers to the act of managing and processing electronic data about disaster cycle from its’ occurrence to progress, response, and plan. However, information about status control, response, recovery from natural and social disaster events, etc. is mainly managed in the structured and unstructured form of reports. Those exist as handouts or hard-copies of reports. Such unstructured form of data is often lost or destroyed due to inefficient management. It is necessary to manage unstructured data for disaster information. In this paper, the Optical Character Recognition approach is used to convert handout, hard-copies, images or reports, which is printed or generated by scanners, etc. into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored in the disaster database system. Gathering and creating disaster information based on Optical Character Recognition for unstructured data is important element as realm of the smart disaster management. In this paper, Korean characters were improved to over 90% character recognition rate by using upgraded OCR. In the case of character recognition, the recognition rate depends on the fonts, size, and special symbols of character. We improved it through the machine learning algorithm. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle such as historical disaster progress, damages, response, and recovery. The expected effect of this research will be able to apply it to smart disaster management and decision making by combining artificial intelligence technologies and historical big data.

Keywords: disaster information management, unstructured data, optical character recognition, machine learning

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Authors: A. Mohamed Mydeen, Pallapa Venkataram

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The learning process needs to be so pervasive to impart the quality in acquiring the knowledge about a subject by making use of the advancement in the field of information and communication systems. However, pervasive learning paradigms designed so far are system automation types and they lack in factual pervasive realm. Providing factual pervasive realm requires subtle ways of teaching and learning with system intelligence. Augmentation of intelligence with pervasive learning necessitates the most efficient way of representing knowledge for the system in order to give the right learning material to the learner. This paper presents a method of representing knowledge for Pervasive Toolroom Maintenance System (PTMS) in which a learner acquires sublime knowledge about the various kinds of tools kept in the toolroom and also helps for effective maintenance of the toolroom. First, we explicate the generic model of knowledge representation for PTMS. Second, we expound the knowledge representation for specific cases of toolkits in PTMS. We have also presented the conceptual view of knowledge representation using ontology for both generic and specific cases. Third, we have devised the relations for pervasive knowledge in PTMS. Finally, events are identified in PTMS which are then linked with pervasive data of toolkits based on relation formulated. The experimental environment and case studies show the accuracy and efficient knowledge representation of toolkits in PTMS.

Keywords: knowledge representation, pervasive computing, agent technology, ECA rules

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

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2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: artificial intelligence, machine learning, deep learning, convolutional neural networks

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Authors: Umamaheswari Shanmugam, Silvia Ronchi

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that can use the large amount and variety of data generated during healthcare services every day; one of the significant advantages of these new technologies is the ability to get experience and knowledge from real-world use and to improve their performance continuously. Healthcare systems and institutions can significantly benefit because the use of advanced technologies improves the efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and protect patients' safety. The evolution and the continuous improvement of software used in healthcare must consider the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device's approval. Still, they are necessary to ensure performance, quality, and safety. At the same time, they can be a business opportunity if the manufacturer can define the appropriate regulatory strategy in advance. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems

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Authors: Elżbieta Turska

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Introduction: One of the most difficult times in a person’s life is adolescence. The experiences in this period may shape the future life of this person to a large extent. This is the reason why many young people experience sadness, dejection, hopelessness, sense of worthlessness, as well as losing interest in various activities and social relationships, all of which are often classified as mood disorders. As many as 15-40% adolescents experience depressed moods and for most of them they resolve and are not carried into adulthood. However, (5-6%) of those affected by mood disorders develop the depressive syndrome and as many as (1-3%) develop full-blown clinical depression. Materials: A large questionnaire was given to 2508 students, aged 13–16 years old, and one of its parts was the Burns checklist, i.e. the standard test for identifying depressed mood. The questionnaire asked about many aspects of the student’s life, it included a total of 53 questions, most of which had subquestions. It is important to note that the data suffered from many problems, the most important of which were missing data and collinearity. Aim: In order to identify the correlates of mood disorders we built predictive models which were then trained and validated. Our aim was not to be able to predict which students suffer from mood disorders but rather to explore the factors influencing mood disorders. Methods: The problems with data described above practically excluded using all classical statistical methods. For this reason, we attempted to use the following Artificial Intelligence (AI) methods: classification trees with surrogate variables, random forests and xgboost. All analyses were carried out with the use of the mlr package for the R programming language. Resuts: The predictive model built by classification trees algorithm outperformed the other algorithms by a large margin. As a result, we were able to rank the variables (questions and subquestions from the questionnaire) from the most to least influential as far as protection against mood disorder is concerned. Thirteen out of twenty most important variables reflect the relationships with parents. This seems to be a really significant result both from the cognitive point of view and also from the practical point of view, i.e. as far as interventions to correct mood disorders are concerned.

Keywords: mood disorders, adolescents, family, artificial intelligence

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Authors: Zabeehullah, Fahim Arif, Yawar Abbas

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Software Defined Network (SDN) is a next genera-tion networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric value. However, IoT network heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because traditional routing algorithms lack the self-adaptions, intelligence and efficient utilization of resources. To some extent, SDN, due its flexibility, and centralized control has managed the IoT complexity and heterogeneity but still Software Defined IoT (SDIoT) lacks intelligence. To address this challenge, we proposed a model called Deep Routing Strategy (DRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Proposed model has also outperformed benchmark routing algorithm (OSPF). Moreover, proposed model provided encouraging results during high dynamic traffic flow.

Keywords: SDN, IoT, DL, ML, DRS

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Authors: Dhurgham Al-Karawi, Nisreen Polus, Shakir Al-Zaidi, Sabah Jassim

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The management of COVID-19 patients based on chest imaging is emerging as an essential tool for evaluating the spread of the pandemic which has gripped the global community. It has already been used to monitor the situation of COVID-19 patients who have issues in respiratory status. There has been increase to use chest imaging for medical triage of patients who are showing moderate-severe clinical COVID-19 features, this is due to the fast dispersal of the pandemic to all continents and communities. This article demonstrates the development of machine learning techniques for the test of COVID-19 patients using Chest X-Ray (CXR) images in nearly real-time, to distinguish the COVID-19 infection with a significantly high level of accuracy. The testing performance has covered a combination of different datasets of CXR images of positive COVID-19 patients, patients with viral and bacterial infections, also, people with a clear chest. The proposed AI scheme successfully distinguishes CXR scans of COVID-19 infected patients from CXR scans of viral and bacterial based pneumonia as well as normal cases with an average accuracy of 94.43%, sensitivity 95%, and specificity 93.86%. Predicted decisions would be supported by visual evidence to help clinicians speed up the initial assessment process of new suspected cases, especially in a resource-constrained environment.

Keywords: COVID-19, chest x-ray scan, artificial intelligence, texture analysis, local binary pattern transform, Gabor filter

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Authors: Soheila Sadeghi

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— The growing integration of Artificial Intelligence (AI) into Human Resources (HR) processes has transformed the way organizations manage recruitment, performance evaluation, and employee engagement. While AI offers numerous advantages—such as improved efficiency, reduced bias, and hyper-personalization—it raises significant concerns about employee well-being, job security, fairness, and transparency. The study examines how AI shapes employee perceptions, job satisfaction, mental health, and retention. Key findings reveal that: (a) while AI can enhance efficiency and reduce bias, it also raises concerns about job security, fairness, and privacy; (b) transparency in AI systems emerges as a critical factor in fostering trust and positive employee attitudes; and (c) AI systems can both support and undermine employee well-being, depending on how they are implemented and perceived. The research introduces an AI-employee well-being Interaction Framework, illustrating how AI influences employee perceptions, behaviors, and outcomes. Organizational strategies, such as (a) clear communication, (b) upskilling programs, and (c) employee involvement in AI implementation, are identified as crucial for mitigating negative impacts and enhancing positive outcomes. The study concludes that the successful integration of AI in HR requires a balanced approach that (a) prioritizes employee well-being, (b) facilitates human-AI collaboration, and (c) ensures ethical and transparent AI practices alongside technological advancement.

Keywords: artificial intelligence, human resources, employee well-being, job satisfaction, organizational support, transparency in AI

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Authors: Angelyn Del Mundo

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Bullying cases are a proliferating issue that schools need to address. This calls for a challenge in providing effective measures to reduce bullying. The study aimed to determine which among the socio-emotional aspects of adolescent males could predict bullying. The respondents of the study were the grades 10 and 11 level and the selection of the respondents was based on the names listed by the teachers and guidance counselors through the Student Nomination Questionnaire. The Bullying Survey Questionnaire Checklist was answered by the respondents to be able to identify their most observed bullying behavior. On the other hand, the level of their mental ability was measured through the use of Otis-Lennon School Ability Test, while their socio-emotional aspects was is classified into 2 contexts: emotional intelligence and personality traits which were determined with the use of Bar-On Emotional Quotient Inventory: Youth Version (BarOn EQ-i:YV) and the Five-Factor Personality Inventory-Children (FFPI-C). Results indicated that majority of the respondents have average level of mental ability and socio-emotional aspects. However, many students have low to markedly low level interpersonal scale. Furthermore, general mood and emotional regulation were found as predictors of bullying behaviors. These findings became the basis for a proposed bullying intervention program.

Keywords: bullying, emotional intelligence, mental ability, personality traits

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Authors: Pichamon Chansuchai

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The study of Thai consumer behavior toward buying goods on the Internet is a survey research. The five-level rating scale and open-ended questionnaire are applied for this research procedure, which has more than 400 random sampling of Thai people aged between 15-40 years old. The summary findings are: The analysis of respondents profile were female 55.3% and male 44.8% , 35.3% aged between 20-30 years old, had been employed 29.5% with average income up to 11,000 baht/month 50.2% and expenditure more than 11,000 baht per month 29.3%. The internet usage behavior of respondents mostly found that objectives of the internet usage are: 1) Communication 93.3% 2) the categories of websites usage was trading 42.8% 3) The marketing mix effected to trading behavior via internet which can be analyzed in term of marketing factor as following: Product focused on product quality was the most influenced factor with average value 4.75. The cheaper price than overview market was the most effect factor to internet shopping with mean value 4.53. The average value 4.67 of the available place that could reduce spending time for shopping. The effective promotion of the buy 1 get 1 was the stimulus factor for internet shopping with mean value 4.60. For hypothesis testing, the different sex has relationship with buying decision. It presented that male and female have vary purchasing decision via internet with value of significant difference 0.05. Furthermore, the variety occupations of respondents related to the use of selected type of website. It also found that the vary of personal occupation effected to the type of website selection dissimilar with value of significant difference 0.05.

Keywords: behavior, internet, consumer, goods

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Authors: Nikita Bharadia, Vikas Gupta, Sushant Koshy

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Mobile phone applications (“apps”) have generated substantial interest among marketers and researchers because of the developments in the smartphone technology and the availability of affordable phones to a large number of consumers. Apps are enabling brands to engage with consumers at any time and any place. This study utilizes a pre-test/post-test experimental design to determine if apps can have a persuasive impact on the consumer attitude towards the brand and her purchase intention. The study also tests the impact of informational vs. interactive style of apps on categories with high and low level of involvement. The results show that for high involvement brands, consumers have a predetermined brand image and apps that satisfy consumer needs through an interactive interface can increase purchase intention. For low involvement brands, while informational apps do not create substantial engagement, interactive apps can increase consumer focus on the brand and establish personal connect with the consumers. This has a positive impact in the attitude towards the brand. These results suggest that understanding how to maximize the consumer interaction with mobile phone apps will be a key topic of future research. This research indicates that managers need to evaluate the how apps can solve consumer needs before investing resources towards digital marketing campaign for their brands, following the global trend to capitalize on the digital platforms.

Keywords: App execution style, high and low involvement categories, mobile marketing, smartphone applications

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Authors: Vrajesh Chokshi

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In last three decades, the global economy is substantially changed. Today, we are living in highly inter-connected world. The global markets are more open and consumers are well informed about products and services. The information technology revolution has broken all barriers in global business. The E-commerce has given opportunities of global trades to corporate. The IT is extensively used in almost all industries. After liberalization in 1992, the Indian economy is also significantly changed. The IT (information technology) and ITES (IT enable services) are extensively used in supply chain management. In India, previously car rental service business was dominated by local organization and operated through local contact. This industry is very lucrative and to catch this opportunity, many new corporate have ventured into e-commerce car rental service business in India. As the market is very competitive, branding is also very important part of marketing strategy. Now, the E-commerce portals those are in car rental business in India have realized the importance of the same and have started usage of all types of communication channel to promote their brand in different Indian markets. At consumer side, the awareness is also being considerably increased due to marketing communication campaign run by these companies. This paper aims to understand effectiveness of branding strategies in car rental business in India and also tries to identify unique promotional strategies to consolidate brand image of this business in different Indian markets.

Keywords: branding strategies, car rental business, CRM (customer relationship management), ITES (information technology enabled services)

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Authors: Mandeep Saini

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The rapid advancement of Artificial Intelligence (AI) is transforming various fields, including intellectual property law. This paper explores the emerging role of AI in interpreting patent claims, a critical and highly specialized area within intellectual property rights. Patent claims define the scope of legal protection granted to an invention, and their precise interpretation is crucial in determining the boundaries of the patent holder's rights. Traditionally, this interpretation has relied heavily on the expertise of patent examiners, legal professionals, and judges. However, the increasing complexity of modern inventions, especially in fields like biotechnology, software, and electronics, poses significant challenges to human interpretation. Introducing AI into patent claim interpretation raises several legal and ethical concerns. This paper addresses critical issues such as the reliability of AI-driven interpretations, the potential for algorithmic bias, and the lack of transparency in AI decision-making processes. It considers the legal implications of relying on AI, particularly regarding accountability for errors and the potential challenges to AI interpretations in court. The paper includes a comparative study of AI-driven patent claim interpretations versus human interpretations across different jurisdictions to provide a comprehensive analysis. This comparison highlights the variations in legal standards and practices, offering insights into how AI could impact the harmonization of international patent laws. The paper proposes policy recommendations for the responsible use of AI in patent law. It suggests legal frameworks that ensure AI tools complement, rather than replace, human expertise in patent claim interpretation. These recommendations aim to balance the benefits of AI with the need for maintaining trust, transparency, and fairness in the legal process. By addressing these critical issues, this research contributes to the ongoing discourse on integrating AI into the legal field, specifically within intellectual property rights. It provides a forward-looking perspective on how AI could reshape patent law, offering both opportunities for innovation and challenges that must be carefully managed to protect the integrity of the legal system.

Keywords: artificial intelligence (ai), patent claim interpretation, intellectual property rights, algorithmic bias, natural language processing, patent law harmonization, legal ethics

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Authors: Sonia Anand Knowlton

Abstract:

As the use of Artificial Intelligence is becoming increasingly integrated in virtually every area of life, the need and interest to logically formalize the law and judicial reasoning is growing tremendously. The study of argumentation frameworks (AFs) provides promise in this respect. AF’s provide a way of structuring human reasoning using a formal system of non-monotonic logic. P.M. Dung first introduced this framework and demonstrated that certain arguments must prevail and certain arguments must perish based on whether they are logically “attacked” by other arguments. Dung labelled the set of prevailing arguments as the “preferred extension” of the given argumentation framework. Trevor Bench-Capon’s Value-based Argumentation Frameworks extended Dung’s AF system by allowing arguments to derive their force from the promotion of “preferred” values. In VAF systems, the success of an attack from argument A to argument B (i.e., the triumph of argument A) requires that argument B does not promote a value that is preferred to argument A. There has been thorough discussion of the application of VAFs to the law within the computer science literature, mainly demonstrating that legal cases can be effectively mapped out using VAFs. This article analyses VAFs from a jurisprudential standpoint to provide a philosophical and theoretical analysis of what VAFs tell the legal community about the judicial reasoning, specifically distinguishing between legal and moral reasoning. It highlights the limitations of using VAFs to account for judicial moral reasoning in theory and in practice.

Keywords: nonmonotonic logic, legal formalization, computer science, artificial intelligence, morality

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Authors: Nikita Katyal, Shaurya Uppal

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This paper tackles the significant challenge of accurately classifying customers within a retailer’s customer base. Accurate classification is essential for developing targeted marketing strategies that effectively engage this important demographic. To address this issue, we propose a method that utilizes Large Language Models (LLMs). By employing LLMs, we analyze the metadata associated with product purchases derived from historical data to identify key product categories that act as distinguishing factors. These categories, such as baby food, eldercare products, or family-sized packages, offer valuable insights into the likely household composition of customers, including families with babies, families with kids/teenagers, families with pets, households caring for elders, or mixed households. We segment high-confidence customers into distinct categories by integrating historical purchase behavior with LLM-powered product classification. This paper asserts that life stage segmentation can significantly enhance e-commerce businesses’ ability to target the appropriate customers with tailored products and campaigns, thereby augmenting sales and improving customer retention. Additionally, the paper details the data sources, model architecture, and evaluation metrics employed for the segmentation task.

Keywords: LLMs, segmentation, product tags, fine-tuning, target segments, marketing communication

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Authors: Guenther Schuh, Christina Koenig, Nico Schoen, Markus Wellensiek

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Identification and selection of appropriate product and manufacturing technologies are key factors for competitiveness and market success of technology-based companies. Therefore many companies perform technology intelligence (TI) activities to ensure the identification of evolving technologies at the right time. Technology monitoring is one of the three base activities of TI, besides scanning and scouting. As the technological progress is accelerating, more and more technologies are being developed. Against the background of limited resources it is therefore necessary to focus TI activities. In this paper, we propose a concept for defining appropriate search fields for technology monitoring. This limitation of search space leads to more concentrated monitoring activities. The concept will be introduced and demonstrated through an anonymized case study conducted within an industry project at the Fraunhofer Institute for Production Technology. The described concept provides a customized monitoring approach, which is suitable for use in technology-oriented companies especially those that have not yet defined an explicit technology strategy. It is shown in this paper that the definition of search fields and search tasks are suitable methods to define topics of interest and thus to direct monitoring activities. Current as well as planned product, production and material technologies as well as existing skills, capabilities and resources form the basis of the described derivation of relevant search areas. To further improve the concept of technology monitoring the proposed concept should be extended during future research e.g. by the definition of relevant monitoring parameters.

Keywords: monitoring radar, search field, technology intelligence, technology monitoring

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Authors: Waleed Afandi

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Artificial intelligence (AI) has rapidly transformed various sectors, including education. This paper explores the integration of AI in education, emphasizing its potential to revolutionize learning processes, enhance teaching methodologies, and personalize education. We examine the historical context of AI in education, current applications, and the potential challenges and ethical considerations associated with its implementation. By reviewing a wide range of literature, this study aims to provide a comprehensive understanding of how AI can be leveraged to improve educational outcomes and the future directions of AI-driven educational innovations. Additionally, the paper discusses the impact of AI on student engagement, teacher support, and administrative efficiency. Case studies highlighting successful AI applications in diverse educational settings are presented, showcasing the practical benefits and real-world implications. The analysis also addresses potential disparities in access to AI technologies and suggests strategies to ensure equitable implementation. Through a balanced examination of the promises and pitfalls of AI in education, this study seeks to inform educators, policymakers, and technologists about the optimal pathways for integrating AI to foster an inclusive, effective, and innovative educational environment.

Keywords: artificial intelligence, education, personalized learning, teaching methodologies, educational outcomes, AI applications, student engagement, teacher support, administrative efficiency, equity in education

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Authors: Murat Erdoğdu, Murat Koçyiğit

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Brand image and brand loyalty are among the most important relational marketing elements for brand owners to be able to set up long – term relationships with their customers and to maintain these relationships. Brand owners improve their brand images with the positive perceptions remaining in the consumers’ minds. In addition, they try to find the customers that are both emotionally and behaviourally faithful to themselves in order to set up long – term relationships. Therefore, the aim of this study is to analyse the effects of the brand image that has a very important role among relational marketing elements on the brand loyalty in terms of the variables such as the perceived value, the trust in brand and the brand satisfaction. In this context, a conceptual model was created to determine the effect of the brand image on the brand loyalty thanks to the Structural Equation Model (SEM). According to this aim and this model, the study was carried out in the scope of the data collected through the questionnaires in Konya with the method of convenience sampling. The results of the research showed that the brand image has positive significant effects on the perceived value and the trust in brand and that the trust in brand has positive significant effects on the brand satisfaction, and that the brand satisfaction has positive significant effects on the brand loyalty. Thus, the hypotheses that the brand image has direct effects on the perceived value and the trust in brand and that the trust in brand has direct effects on the brand satisfaction and that the brand satisfaction has direct effects on the brand loyalty were supported. In addition, the findings about whether the perceived value has a significant effect on the brand satisfaction were also acquired.

Keywords: brand image, brand loyalty, perceived value, satisfaction, trust

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Authors: S. Sharan Sundar, Nithish B. Moudhgalya, Nidhi Bhandari, Vineeth Vijayaraghavan

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Automatic Short Answer Grading is one of the prime applications of artificial intelligence in education. Several approaches involving the utilization of selective handcrafted features, graphical matching techniques, concept identification and mapping, complex deep frameworks, sentence embeddings, etc. have been explored over the years. However, keeping in mind the real-world application of the task, these solutions present a slight overhead in terms of computations and resources in achieving high performances. In this work, a simple and effective solution making use of elemental features based on statistical, linguistic properties, and word-based similarity measures in conjunction with tree-based classifiers and regressors is proposed. The results for classification tasks show improvements ranging from 1%-30%, while the regression task shows a stark improvement of 35%. The authors attribute these improvements to the addition of multiple similarity scores to provide ensemble of scoring criteria to the models. The authors also believe the work could reinstate that classical natural language processing techniques and simple machine learning models can be used to achieve high results for short answer grading.

Keywords: artificial intelligence, intelligent systems, natural language processing, text mining

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Authors: Sidika Di̇lşad Kaya, Ahmet Seli̇m Kaya, Ibrahi̇m Eri̇k, Havva Yaldiz, Yalçin Kaya

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The aim of this study was to reveal the effects of right brain development on reading, comprehension, learning and concentration levels and rapid processing skills in students with low vision and students with standard development, and to explore the effects of right and left brain integration on students' academic success and the permanence of the learned knowledge. A total of 68 students with a mean age of 10.01±0.12 were included in the study, 58 of them with standard development, 9 partially visually impaired and 1 totally visually disabled student. The student with a total visual impairment could not participate in the reading speed test due to her total visual impairment. The following data were measured in the participant students before the project; Reading speed measurement in 1 minute, Reading comprehension questions, Burdon attention test, 50 questions of math quiz timed with a stopwatch. Participants were trained for 3 weeks, 5 days a week, for a total of two hours a day. In this study, right-brain developing exercises were carried out with the use of an abacus, and it was aimed to develop both mathematical and attention of students with questions prepared with numerical data taken from fairy tale activities. Among these problems, the study was supported with multiple-choice, 5W (what, where, who, why, when?), 1H (how?) questions along with true-false and fill-in-the-blank activities. By using memory cards, students' short-term memories were strengthened, photographic memory studies were conducted and their visual intelligence was supported. Auditory intelligence was supported by aiming to make calculations by using the abacus in the minds of the students with the numbers given aurally. When calculating the numbers by touching the real abacus, the development of students' tactile intelligence is enhanced. Research findings were analyzed in SPSS program, Kolmogorov Smirnov test was used for normality analysis. Since the variables did not show normal distribution, Wilcoxon test, one of the non-parametric tests, was used to compare the dependent groups. Statistical significance level was accepted as 0.05. The reading speed of the participants was 83.54±33.03 in the pre-test and 116.25±38.49 in the post-test. Narration pre-test 69.71±25.04 post-test 97.06±6.70; BURDON pretest 84.46±14.35 posttest 95.75±5.67; rapid math processing skills pretest 90.65±10.93, posttest 98.18±2.63 (P<0.05). It was determined that the pre-test and post-test averages of students with typical development and students with low vision were also significant for all four values (p<0.05). As a result of the data obtained from the participants, it is seen that the study was effective in terms of measurement parameters, and the findings were statistically significant. Therefore, it is recommended to use the method widely.

Keywords: Abacus, reading speed, multiple intelligences, right brain training, visually impaired

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Authors: Ju Shan Lin

Abstract:

The challenges for traditional industries in Taiwan the past few years are the changes of overall domestic and foreign industry structure, the entrepreneurs not only need to keep on improving their profession skills but also continuously research and develop new products. It is also necessary for the all traditional industries to keep updating the business strategy, let the enterprises continue to progress, and won't be easily replaced by the other industries. The traditional industry in Taiwan attach great importance to the field of enterprises upgrading and innovation in recent years, by the enterprise innovation and transformation can enhance the overall business situation also enable them to obtain more additional profits than in the past. Except the original industry structure's need to transform and upgrade, the brand's business and marketing strategy are also essential. This study will take a certain plastic company as case analysis, for the brand promotion of traditional industries, brand values and business innovation model for further exploration. It will also be mentioned that the other traditional industries cases which were already achieved success on the enterprise's upgrading and innovation, at the same time, the difficulties which they faced with and the way they overcome will be explored as well. This study will use the case study method combined with expert interviews to discuss and analyze this certain plastic company's current business situation, the existing products and the possible trends in the future. Looking forward to providing an innovative business model that will enable this plastic company to upgrade its corporate image and the brand could transform successfully.

Keywords: brand marketing strategy, enterprise upgrade, industrial transformation, traditional industry

Procedia PDF Downloads 239