Search results for: business intelligence readiness model

Authors: Wildan Maulana, Machfudz Sa'idi

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Yogyakarta is a city with the most students in Indonesia, more than 250 thousand students living in Yogyakarta and more than 140 universities in Yogyakarta. Therefore, Yogyakarta is a very strategic place for the culinary business. Food is a basic requirement of all living things, and the tasty food and cheap is the target of almost all students. The objective of this paper is to give an idea and the innovation of culinary business in Yogyakarta who apply the concept sociopreneur and technology as a tool to facilitate the course of this business. KedaiKampus is a startup that brings the food business operators such as food stalls, restaurants or angkringan (a traditional restaurant of Indonesia) and people who want to find the food with the best price and the best taste. The uniqueness of this business is offered weekly and monthly food packages for students in particular or for everyone who needs and will be delivered to their homes each every hour meal. KedaiKampus is also a marketspace for industrial and culinary houses, using technology based mobile application and website will allow the food industry to connect them with customers, but it also allows them to know the customer's desire for food trending in the market. The application to be developed is designed for ease of access to customers in finding their favorite foods and convenience for the culinary home to create amazing culinary innovation.

Keywords: marketplace, sociopreneur, culinary, meal

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Authors: Gokhan Egilmez, Erika Hatfield, Julie Turner

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In this study, we investigated the impacts of service-learning integration on an undergraduate level business analytics course from multiple perspectives, including academic proficiency, community awareness, engagement, social responsibility, and reflection. We assessed the impact of the service-learning experience by using a survey developed primarily based on the literature review and secondarily on an ad hoc group of researchers. Then, we implemented the survey in two sections, where one of the sections was a control group. We compared the results of the empirical survey visually and statistically.

Keywords: business analytics, service learning, experiential education, statistical analysis, survey research

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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: Éva Pintér, Péter Bagó, Nikolett Deutsch, Miklós Hetényi

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The process of digital transformation has a profound influence on all sectors of the worldwide economy and the business environment. The influence of blockchain technology can be observed in the digital economy and e-government, rendering it an essential element of a nation's growth strategy. The banking industry is experiencing significant expansion and development of financial technology firms. Utilizing developing technologies such as artificial intelligence (AI), machine learning (ML), and big data (BD), these entrants are offering more streamlined financial solutions, promptly addressing client demands, and presenting a challenge to incumbent institutions. The advantages of digital transformation are evident in the corporate realm, and firms that resist its adoption put their survival at risk. The advent of digital technologies has revolutionized the business environment, streamlining processes and creating opportunities for enhanced communication and collaboration. Thanks to the aid of digital technologies, businesses can now swiftly and effortlessly retrieve vast quantities of information, all the while accelerating the process of creating new and improved products and services. Big data analytics is generally recognized as a transformative force in business, considered the fourth paradigm of science, and seen as the next frontier for innovation, competition, and productivity. Big data, an emerging technology that is shaping the future of the banking sector, offers numerous advantages to banks. It enables them to effectively track consumer behavior and make informed decisions, thereby enhancing their operational efficiency. Banks may embrace big data technologies to promptly and efficiently identify fraud, as well as gain insights into client preferences, which can then be leveraged to create better-tailored products and services. Moreover, the utilization of big data technology empowers banks to develop more intelligent and streamlined models for accurately recognizing and focusing on the suitable clientele with pertinent offers. There is a scarcity of research on big data analytics in the banking industry, with the majority of existing studies only examining the advantages and prospects associated with big data. Although big data technologies are crucial, there is a dearth of empirical evidence about the role of big data analytics (BDA) capabilities in bank performance. This research addresses a gap in the existing literature by introducing a model that combines the resource-based view (RBV), the technical organization environment framework (TOE), and dynamic capability theory (DC). This study investigates the influence of Big Data Analytics (BDA) utilization on the performance of market and risk management. This is supported by a comparative examination of Hungarian mobile banking services.

Keywords: big data, digital transformation, dynamic capabilities, mobile banking

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Authors: Susan P. Teru, Jerome Nyameh

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Many contemporary organizations are placing a greater emphasis on business enterprise systems as a means of generating higher levels of economic development and sustainability. Many business research and literature has also concur that enterprise drive economic development, giving little or no credit to social enterprise, whose profit is reinvest to the community development compare to the business enterprise that share their profit to shareholders. Economic development and corporate sustainability includes economic policies that affect the beneficiaries of the economic entity and how it support corporate sustainability as a multifaceted concept that requires organizational change and adaptation on different levels. In this paper, we provide a closer examination of this suggested link between the social enterprise orientation of an organization and the pursuit of corporate sustainability. We suggest that producing social enterprise increments may be best achieved by orienting social enterprise entrepreneurs system to promote economic development and corporate sustainability, which is the new approach to organizational excellent. To this end, we describe a new approach to the social enterprise process that includes social entrepreneur and the key drivers of economic development and corporate sustainability at each stage. We present a model of social enterprise that incorporates the main ideas of the paper and suggests a new perspective for thinking about how to foster and manage social enterprise to achieve high levels of economic development and corporate sustainability as a new ways of achieving organizational excellence. Specifically, we seek to assess (1) what constitutes a corporate sustainability-oriented organization culture, (2) whether it is possible for organizations to display a unified corporate sustainability as a result of social enterprise (3) whether organizations can become more sustainable through social enterprise change.

Keywords: social enterprise orientation, organization, the pursuit of corporate sustainability, business and management

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Authors: Yu Chen

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In line with existing philosophical approaches, this research proposes a conceptual model with an innovative spatial vision and aesthetic principles for Artificial Intelligence (AI) application in brand advertising. The model first identifies the major constituencies in contemporary advertising on three spatial levels—physical, psychological, and symbolic. The model further incorporates the relationships among AI, aesthetics, branding, and advertising and their interactions with the major actors in all spaces. It illustrates that AI may follow the aesthetic principles-- beauty, elegance, and simplicity-- to reinforce brand identity and consistency in advertising, to collaborate with stakeholders, and to satisfy different advertising objectives on each level. It proposes that, with aesthetic guidelines, AI may assist consumers to emerge into the physical, psychological, and symbolic advertising spaces and helps transcend the tangible advertising messages to meaningful brand symbols. Conceptually, the research illustrates that even though consumers’ engagement with brand mostly begins with physical advertising and later moves to psychological-symbolic, AI-assisted advertising should start with the understanding of brand symbolic-psychological and consumer aesthetic preferences before the physical design to better resonate. Limits of AI and future AI functions in advertising are discussed.

Keywords: AI, spatial, aesthetic, brand advertising

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Authors: Leah Ning

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This paper discusses the development of a machine learning algorithm that accurately detects metastatic breast cancer (cancer has spread elsewhere from its origin part) in selected images that come from pathology scans of lymph node sections. Being able to develop an accurate artificial intelligence (AI) algorithm would help significantly in breast cancer diagnosis since manual examination of lymph node scans is both tedious and oftentimes highly subjective. The usage of AI in the diagnosis process provides a much more straightforward, reliable, and efficient method for medical professionals and would enable faster diagnosis and, therefore, more immediate treatment. The overall approach used was to train a convolution neural network (CNN) based on a set of pathology scan data and use the trained model to binarily classify if a new scan were benign or malignant, outputting a 0 or a 1, respectively. The final model’s prediction accuracy is very high, with 100% for the train set and over 70% for the test set. Being able to have such high accuracy using an AI model is monumental in regard to medical pathology and cancer detection. Having AI as a new tool capable of quick detection will significantly help medical professionals and patients suffering from cancer.

Keywords: breast cancer detection, AI, machine learning, algorithm

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Authors: Rowanda Daoud Ahmed, Mansoor Abdulhak, Muhammad Azeem Afzal, Sezer Filiz, Usama Ahmad Mughal

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This paper discusses important aspects of AI in the healthcare domain. The increase of data in healthcare both in size and complexity, opens more room for artificial intelligence applications. Our focus is to review the main AI methods within the scope of the health care domain. The results of the review show that recommendations for diagnosis and recommendations for treatment, patent engagement, and administrative tasks are the key applications of AI in healthcare. Understanding the potential of AI methods in the domain of healthcare would benefit healthcare practitioners and will improve patient outcomes.

Keywords: AI in healthcare, technologies of AI, neural network, future of AI in healthcare

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Authors: Cahit Perkgöz

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Artificial intelligence has brought new approaches to solving problems in almost every research field in recent years. One of these topics is shape design and optimization, which has the possibility of applications in many fields, such as nanotechnology and electronics. A properly constructed cost function can eliminate the need for labeled data required in deep learning and create desired shapes. In this work, the network parameters are optimized differentially, which differs from traditional approaches. The methods are tested for physics-related structures and successful results are obtained. This work is supported by Eskişehir Technical University scientific research project (Project No: 20ADP090)

Keywords: deep learning, shape design, optimization, artificial intelligence

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Authors: M. Graus, K. Westhoff, X. Xu

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The climate change causes a change in all aspects of society. While the expansion of renewable energies proceeds, industry could not be convinced based on general studies about the potential of demand side management to reinforce smart grid considerations in their operational business. In this article, a procedure model for a case-specific data-driven decision support for industrial energy management based on a holistic data analytics approach is presented. The model is executed on the example of the strategic decision problem, to integrate the aspect of renewable energies into industrial energy management. This question is induced due to considerations of changing the electricity contract model from a standard rate to volatile energy prices corresponding to the energy spot market which is increasingly more affected by renewable energies. The procedure model corresponds to a data analytics process consisting on a data model, analysis, simulation and optimization step. This procedure will help to quantify the potentials of sustainable production concepts based on the data from a factory. The model is validated with data from a printer in analogy to a simple production machine. The overall goal is to establish smart grid principles for industry via the transformation from knowledge-driven to data-driven decisions within manufacturing companies.

Keywords: data analytics, green production, industrial energy management, optimization, renewable energies, simulation

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Authors: Deborah O. Ajumobi, Michael Kyobe

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Studies show that women entrepreneurs are constrained and faced with challenges that inhibit the growth and performance of their businesses. However, with their human competencies, mobile technology and the appropriate business strategy, women-led SMEs can steer their businesses to better performance. While the need for SMEs to align these three elements has been suggested, there is limited knowledge on how SMEs can achieve this and no studies to the authors’ knowledge have examined this in women-led SMEs. This study therefore seeks to fill this gap by investigating how Women-led SMEs can best align these three elements to enhance business performance. In light of this, extensive literature review and theoretical work on the phenomenon has been conducted. Given the existence of the interplay between these three elements, we argue that the perspective of alignment as gestalts is most appropriate in determining the best way women-Led SMEs may align these aspects.

Keywords: women-led SMEs, human Competencies, mobile technology, business strategy, alignment

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Authors: Janos Farkas, Maria Czeller, Ildiko Tar

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A remarkable feature of today’s language teaching is the learners’ language learning motivation. It is always considered as a very important factor and has been widely discussed and investigated. This paper aims to present a research study conducted in higher education institutions among students majoring in business and administration in Hungary. The aim of the research was to investigate the motivational factors of students learning languages for business purposes and set up a multivariate statistical model of language learning motivation, and examine the model's main components by different social background variables. The research question sought to answer the question of whether the motivation of students of business learning LSP could be characterized through some main components. The principal components of LSP have been created, and the correlations with social background variables have been explored. The main principal components of learning a language for business purposes were "professional future", "abroad", "performance", and "external". In the online voluntary questionnaire, 28 questions were asked about students’ motivational attitudes. 449 students have filled in the questionnaire. Descriptive statistical calculations were performed, then the difference between the highest and lowest mean was analyzed by one-sample t-test. The assessment of LSP learning was examined by one-way analysis of variance and Tukey post-hoc test among students of parents with different qualifications. The correlations between student motivation statements and various social background variables and other variables related to LSP learning motivation (gender, place of residence, mother’s education, father’s education, family financial situation, etc.) have also been examined. The attitudes related to motivation were seperated by principal component analysis, and then the different language learning motivation between socio-economic variables and other variables using principal component values were examined using an independent two-sample t-test. The descriptive statistical analysis of language learning motivation revealed that students learn LSP because this knowledge will come in handy in the future. It can be concluded that students consider learning the language for business purposes to be essential and see its future benefits. Therefore, LSP teaching has an important role and place in higher education. The results verify the second linguistic motivational self-system where the ideal linguistic self embraces the ideas and desires that the foreign language learner wants to achieve in the future. One such desire is to recognize that students will need technical language skills in the future, and it is a powerful motivation for them to learn a language.

Keywords: higher education, language learning motivation, LSP, statistical analysis

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Authors: Cristian Pauna

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With few steps away from the 2020, investments in financial markets is a common activity nowadays. In the electronic trading environment, the automated investment software has become a major part in the business intelligence system of any modern financial company. The investment decisions are assisted and/or made automatically by computers using mathematical algorithms today. The complexity of these algorithms requires computer assistance in the investment process. This paper will present several investment strategies that can be automated with algorithmic trading for Deutscher Aktienindex DAX30. It was found that, based on several price action mathematical models used for high-frequency trading some investment strategies can be optimized and improved for automated investments with good results. This paper will present the way to automate these investment decisions. Automated signals will be built using all of these strategies. Three major types of investment strategies were found in this study. The types are separated by the target length and by the exit strategy used. The exit decisions will be also automated and the paper will present the specificity for each investment type. A comparative study will be also included in this paper in order to reveal the differences between strategies. Based on these results, the profit and the capital exposure will be compared and analyzed in order to qualify the investment methodologies presented and to compare them with any other investment system. As conclusion, some major investment strategies will be revealed and compared in order to be considered for inclusion in any automated investment system.

Keywords: Algorithmic trading, automated investment systems, limit conditions, trading principles, trading strategies

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Authors: Amy Gooden

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Various modes of consent have been utilized in health research, but open consent has not been explored in South Africa’s AI research context. Open consent entails the sharing of data without assurances of privacy and may be seen as an attempt to marry open science with informed consent. Because all potential uses of data are unknown, it has been questioned whether consent can be informed. Instead of trying to adapt existing modes of consent, why not adopt a new perspective? This is what open consent proposes and what this research will explore in AI health research in South Africa.

Keywords: artificial intelligence, consent, health, law, research, South Africa

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Authors: Ivanka Valova

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This paper is aimed at using a multi-agent approach to monitor and diagnose a biotechnological system in order to validate certain control actions depending on the process development and the operating conditions. A multi-agent system is defined as a network of interacting software modules that collectively solve complex tasks. Remote monitoring and control of biotechnological processes is a necessity when automated and reliable systems operating with no interruption of certain activities are required. The advantage of our approach is in its flexibility, modularity and the possibility of improving by acquiring functionalities through the integration of artificial intelligence.

Keywords: multi-agent approach, artificial intelligence, biotechnological processes, anaerobic biodegradation

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Authors: Simone Cinquemani, Lorenzo Mantovani, Aleksander Dabek

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New technologies in agriculture are opening new challenges and new opportunities. Among these, certainly, robotics, vision, and artificial intelligence are the ones that will make a significant leap, compared to traditional agricultural techniques, possible. In particular, the indoor farming sector will be the one that will benefit the most from these solutions. Vertical farming is a new field of research where mechanical engineering can bring knowledge and know-how to transform a highly labor-based business into a fully autonomous system. The aim of the research is to develop a multi-purpose, modular, and perfectly integrated platform for crop production in indoor vertical farming. Activities will be based both on hardware development such as automatic tools to perform different activities on soil and plants, as well as research to introduce an extensive use of monitoring techniques based on machine learning algorithms. This paper presents the preliminary results of a research project of a vertical farm living lab designed to (i) develop and test vertical farming cultivation practices, (ii) introduce a very high degree of mechanization and automation that makes all processes replicable, fully measurable, standardized and automated, (iii) develop a coordinated control and management environment for autonomous multiplatform or tele-operated robots in environments with the aim of carrying out complex tasks in the presence of environmental and cultivation constraints, (iv) integrate AI-based algorithms as decision support system to improve quality production. The coordinated management of multiplatform systems still presents innumerable challenges that require a strongly multidisciplinary approach right from the design, development, and implementation phases. The methodology is based on (i) the development of models capable of describing the dynamics of the various platforms and their interactions, (ii) the integrated design of mechatronic systems able to respond to the needs of the context and to exploit the strength characteristics highlighted by the models, (iii) implementation and experimental tests performed to test the real effectiveness of the systems created, evaluate any weaknesses so as to proceed with a targeted development. To these aims, a fully automated laboratory for growing plants in vertical farming has been developed and tested. The living lab makes extensive use of sensors to determine the overall state of the structure, crops, and systems used. The possibility of having specific measurements for each element involved in the cultivation process makes it possible to evaluate the effects of each variable of interest and allows for the creation of a robust model of the system as a whole. The automation of the laboratory is completed with the use of robots to carry out all the necessary operations, from sowing to handling to harvesting. These systems work synergistically thanks to the knowledge of detailed models developed based on the information collected, which allows for deepening the knowledge of these types of crops and guarantees the possibility of tracing every action performed on each single plant. To this end, artificial intelligence algorithms have been developed to allow synergistic operation of all systems.

Keywords: automation, vertical farming, robot, artificial intelligence, vision, control

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Authors: Adedayo Johnson Ogungbile, Ayodeji Emmanuel Oke, Oluwaseyi Alabi Awodele

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This study examines the role of emotional intelligence (EI) as a predictor of job satisfaction within the Nigerian construction industry. Utilizing a methodology that combines mean comparison and correlation analysis, the research explores how EI influences job satisfaction across diverse demographic and professional categories. The construction industry, known for its dynamic and often challenging work environment, provides a unique context to investigate how EI contributes to employee satisfaction. The findings reveal a significant positive correlation between EI and job satisfaction across the industry. Gender-based analysis shows that male employees typically report higher EI and job satisfaction levels compared to their female counterparts, although the impact of EI on job satisfaction is more substantial among women. The study further explores the relationship between trait EI and specific job satisfaction categories, identifying a general positive association with overall job satisfaction but not with supervisor-related satisfaction. Employees are categorized into four EI classes, consistently showing that higher EI levels correspond to greater job satisfaction. These findings align with existing literature, underscoring EI's pivotal role in enhancing job satisfaction in the construction sector. The study concludes that fostering EI among construction industry professionals can lead to improved job satisfaction and performance. Consequently, organizations are encouraged to integrate EI development into their professional growth programs to cultivate a more satisfied and effective workforce. In essence, this research highlights the importance of EI as a key predictor of job satisfaction in the Nigerian construction industry, providing valuable insights for both industry stakeholders and researchers into the benefits of prioritizing emotional intelligence in this high-stakes environment.

Keywords: emotional intelligence, job satisfaction, construction industry, workforce productivity, demographics

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Authors: Sidi Yang, Haiyi Zhang

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Twitter is a microblogging platform, where millions of users daily share their attitudes, views, and opinions. Using a probabilistic Latent Dirichlet Allocation (LDA) topic model to discern the most popular topics in the Twitter data is an effective way to analyze a large set of tweets to find a set of topics in a computationally efficient manner. Sentiment analysis provides an effective method to show the emotions and sentiments found in each tweet and an efficient way to summarize the results in a manner that is clearly understood. The primary goal of this paper is to explore text mining, extract and analyze useful information from unstructured text using two approaches: LDA topic modelling and sentiment analysis by examining Twitter plain text data in English. These two methods allow people to dig data more effectively and efficiently. LDA topic model and sentiment analysis can also be applied to provide insight views in business and scientific fields.

Keywords: text mining, Twitter, topic model, sentiment analysis

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Authors: Ässia Boukhatmi, Roger Nyffenegger

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The expansion of solar energy as a means to address the climate crisis is undisputed, but the increasing number of new photovoltaic (PV) modules being put on the market is simultaneously leading to increased challenges in terms of managing the growing waste stream. Many of the discarded modules are still fully functional but are often damaged by improper handling after disassembly or not properly tested to be considered for a second life. In addition, the collection rate for dismantled PV modules in several European countries is only a fraction of previous projections, partly due to the increased number of illegal exports. The underlying problem for those market imperfections is an insufficient data exchange between the different actors along the PV value chain, as well as the limited traceability of PV panels during their lifetime. As part of the Horizon 2020 project CIRCUSOL, an asset database prototype was developed to tackle the described problems. In an iterative process applying the design science research methodology, different business models, as well as the technical implementation of the database, were established and evaluated. To explore the requirements of different stakeholders for the development of the database, surveys and in-depth interviews were conducted with various representatives of the solar industry. The proposed database prototype maps the entire value chain of PV modules, beginning with the digital product passport, which provides information about materials and components contained in every module. Product-related information can then be expanded with performance data of existing installations. This information forms the basis for the application of data analysis methods to forecast the appropriate end-of-life strategy, as well as the circular economy potential of PV modules, already before they arrive at the recycling facility. The database prototype could already be enriched with data from different data sources along the value chain. From a business model perspective, the database offers opportunities both in the area of reuse as well as with regard to the certification of sustainable modules. Here, participating actors have the opportunity to differentiate their business and exploit new revenue streams. Future research can apply this approach to further industry and product sectors, validate the database prototype in a practical context, and can serve as a basis for standardization efforts to strengthen the circular economy.

Keywords: business model, circular economy, database, design science research, solar industry

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Authors: Siyu Wang, Anthony Ward

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Emotional intelligence (EI) is a well-established personal characteristic. It has been viewed as a critical factor which can influence an individual's academic achievement, ability to work and potential to succeed. When working in a group, EI is fundamentally connected to the group members' interaction and ability to work as a team. The ability of a group member to intelligently perceive and understand own emotions (Intrapersonal EI), to intelligently perceive and understand other members' emotions (Interpersonal EI), and to intelligently perceive and understand emotions between different groups (Cross-boundary EI) can be considered as Group emotional intelligence (Group EI). In this research, a more representative Group EI measurement approach, which incorporates the information of the composition of a group and an individual’s role in that group, is proposed. To demonstrate the claim of being more representative Group EI measurement approach, this study adopts a multi-method research design, involving a combination of both qualitative and quantitative techniques to establish a metric of Group EI. From the results, it can be concluded that by introducing the weight coefficient of each group member on group work into the measurement of Group EI, Group EI will be more representative and more capable of understanding what happens during teamwork than previous approaches.

Keywords: case study, emotional intelligence, group EI, multi-method research

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Authors: Abdellatif Amouri, Sami Boudabbous

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The current study provides insights on the importance of entrepreneurial coaching as a source of developing entrepreneurial intentions among entrepreneurs and a determinant factor of business creation process and growth. Coaching, which implies exchange of adequate information and a mutual understanding between entrepreneurs and their partners, requires a better mutual knowledge of the representations and the perceptions of ideas which are widely present in their dealings and transactions. Therefore, to analyze entrepreneurs’ perceptions of business creation, we addressed a survey questionnaire to a group of Tunisian entrepreneurs and experts in business creation to indicate their level of approval concerning the prominence of coaching. The factor analysis indicates that more than 60% of the respondents believe that each statement reflects an aspect of coaching, with no bias to its position in the entrepreneurial process. Therefore, the image drawn from our respondents’ perceptions is that an entrepreneur is rather "constructed" and "shaped" by multiple apprenticeships both before and during the entrepreneurial act, through an accompaniment process and within interactions with trainers, consultants or professionals in starting a business. Similarly, the results indicate that the poor support structures and lack of accompaniment procedures stand as an obstacle impeding the development of entrepreneurial intention among business creators.

Keywords: Entrepreneurial Behavior, Entrepreneurial Coaching, Entrepreneurial Intention, Perceptions, Venture Creation

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Authors: Sarah Irani, Meisam Modarresi

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Lifestyle entrepreneurship allows the business owner to create a business activity that aligns with their values, interests, and motivations. Examining the views and experiences of lifestyle entrepreneurs has an essential impact on the growth of the entrepreneurial economy and the concept of entrepreneurship. The primary purpose of this research is to discover the main and secondary influencing aspects of lifestyle entrepreneurship. This research is qualitative and tries to develop research in this field by presenting a framework from the literature. This study can provide a clear picture of lifestyle entrepreneurship. The results showed that lifestyle entrepreneurship is influenced by four main aspects.

Keywords: entrepreneurship, entrepreneurs, innovation, lifestyle entrepreneurship, small businesses development

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Authors: Ulrike Dowie, Ralph Grothmann

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Rational planning decisions are based upon forecasts. Precise forecasting has, therefore, a central role in business. The prediction of customer demand is a prime example. This paper introduces recurrent neural networks to model customer demand and combines the forecast with uncertainty measures to derive decision support of the demand planning department. It identifies and describes the keys to the successful implementation of an AI-based solution: bringing together data with business knowledge, AI methods, and user experience, and applying agile software development practices.

Keywords: agile software development, AI project success factors, deep learning, demand forecasting, forecast uncertainty, neural networks, supply chain management

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Authors: Natnicha Hasoontree

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The purpose of this paper was to study the relationship between brand value and perceived brand quality of television viewers in Bangkok towards the television business in Thailand. The population included television viewers in Bangkok, Thailand. A probability sampling technique was performed to get a sample group that included 500 respondents. Taro Yamane technique was utilized to get a proper sample size. A five Likert scale questionnaire was designed specifically to investigate brand value and perceived brand quality from the perspectives of television viewers in Bangkok. The findings implied that consumers in Bangkok attached a high importance towards the brand equity of television companies that comprised brand ability, brand reputation, brand credibility, and business ethics. Perceived brand quality received high rank in all aspects.

Keywords: brand value, perceived brand quality, television business, television viewers

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Authors: Ichiro Takahashi

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One of the major assumptions of mainstream macroeconomics is the path independence of capital stock. This paper challenges this assumption by employing an agent-based approach. The simulation results showed the existence of multiple "quasi-steady state" equilibria of the capital stock, which may cast serious doubt on the validity of the assumption. The finding would give a better understanding of many phenomena that involve hysteresis, including the causes of poverty. The "market-clearing view" has been widely shared among major schools of macroeconomics. They understand that the capital stock, the labor force, and technology, determine the "full-employment" equilibrium growth path and demand/supply shocks can move the economy away from the path only temporarily: the dichotomy between the short-run business cycles and the long-run equilibrium path. The view then implicitly assumes the long-run capital stock to be independent of how the economy has evolved. In contrast, "Old Keynesians" have recognized fluctuations in output as arising largely from fluctuations in real aggregate demand. It will then be an interesting question to ask if an agent-based macroeconomic model, which is known to have path dependence, can generate multiple full-employment equilibrium trajectories of the capital stock in the super-long run. If the answer is yes, the equilibrium level of capital stock, an important supply-side factor, would no longer be independent of the business cycle phenomenon. This paper attempts to answer the above question by using the agent-based macroeconomic model developed by Takahashi and Okada (2010). The model would serve this purpose well because it has neither population growth nor technology progress. The objective of the paper is twofold: (1) to explore the causes of long-term business cycle, and (2) to examine the super-long behaviors of the capital stock of full-employment economies. (1) The simulated behaviors of the key macroeconomic variables such as output, employment, real wages showed widely diversified macro-economies. They were often remarkably stable but exhibited both short-term and long-term fluctuations. The long-term fluctuations occur through the following two adjustments: the quantity and relative cost adjustments of capital stock. The first one is obvious and assumed by many business cycle theorists. The reduced aggregate demand lowers prices, which raises real wages, thereby decreasing the relative cost of capital stock with respect to labor. (2) The long-term business cycles/fluctuations were synthesized with the hysteresis of real wages, interest rates, and investments. In particular, a sequence of the simulation runs with a super-long simulation period generated a wide range of perfectly stable paths, many of which achieved full employment: all the macroeconomic trajectories, including capital stock, output, and employment, were perfectly horizontal over 100,000 periods. Moreover, the full-employment level of capital stock was influenced by the history of unemployment, which was itself path-dependent. Thus, an experience of severe unemployment in the past kept the real wage low, which discouraged a relatively costly investment in capital stock. Meanwhile, a history of good performance sometimes brought about a low capital stock due to a high-interest rate that was consistent with a strong investment.

Keywords: agent-based macroeconomic model, business cycle, hysteresis, stability

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Authors: Ayanda Magida

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The paper aims to provide a critical review of the current state-of-the-art literature on emerging digital business theories. They are specifically focusing on the emergent theories on digital transformation, digitization, and digitalization and their importance in the global south. Digital business is an emergent field that cuts across the different existing disciplines. The paper is threefold- to provide the conceptual and theoretical definition of the DT, digitization and digitization. There is a growing need to provide some of the differences between digitalization, digitization and digital transformation from a theoretical and conceptual basis. These tend to be confused and often use interchangeably the second aim is to focus on the emerging theories on digital transformation and digital business. Finally, the paper provides some critical review of the importance of scholarship in the field from the global south. The systematic review of the literature was conducted through the different research databases to provide some of the major theories in the field of digital business and critically argue for the global south stance. Much of the research on the development and adoption of digital technologies, specifically digital transformation, has been done in the west and developed countries. There is thus a dearth of research conducted in developing countries and the global south.

Keywords: digital transformation, digitization, digital business, digitalization

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Authors: Viloria Marin Hermes, Paredes Santiago Maritza, Viloria Paredes Jonathan

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The aim of this study is to assess the prevalence of Burnout syndrome in a sample of 600 professors at the University of Guayaquil (Ecuador) using the Maslach Burnout Inventory (M.B.I.). In addition, assessment was made of the effects on health from professional burnout using the General Health Questionnaire (G.H.Q.-28), and the influence of Emotional Intelligence on prevention of its symptoms using the Spanish version of the Trait Meta-Mood Scale (T.M.M.S.-24). After confirmation of the underlying factor structure, the three measurement tools showed high levels of internal consistency, and specific cut-off points were proposed for the group of Latin American academics in the M.B.I. Statistical analysis showed the syndrome is present extensively, particularly on medium levels, with notably low scores given for Professional Self-Esteem. The application of Canonical Correspondence Analysis revealed that low levels of self-esteem are related to depression, with a lack of personal resources related to anxiety and insomnia, whereas the ability to perceive and control emotions and feelings improves perceptions of professional effectiveness and performance.

Keywords: burnout, academics, emotional intelligence, general health, canonical correspondence analysis

Procedia PDF Downloads 370

Authors: Delphine Ribes Lemay, Valentine Bernasconi, André Andrade, Lara DéFayes, Mathieu Salzmann, FréDéRic Kaplan, Nicolas Henchoz

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Digitization and preservation of large heritage induce high maintenance costs to keep up with the technical standards and ensure sustainable access. Creating impactful usage is instrumental to justify the resources for long-term preservation. The Museum für Gestaltung of Zurich holds one of the biggest poster collections of the world from which 52’000 were digitised. In the process of building a digital installation to valorize the collection, one objective was to develop an algorithm capable of predicting the next poster to show according to the ones already displayed. The work presented here describes the steps to build an algorithm able to automatically create sequences of posters reflecting associations performed by curator and professional designers. The exposed challenge finds similarities with the domain of song playlist algorithms. Recently, artificial intelligence techniques and more specifically, deep-learning algorithms have been used to facilitate their generations. Promising results were found thanks to Recurrent Neural Networks (RNN) trained on manually generated playlist and paired with clusters of extracted features from songs. We used the same principles to create the proposed algorithm but applied to a challenging medium, posters. First, a convolutional autoencoder was trained to extract features of the posters. The 52’000 digital posters were used as a training set. Poster features were then clustered. Next, an RNN learned to predict the next cluster according to the previous ones. RNN training set was composed of poster sequences extracted from a collection of books from the Gestaltung Museum of Zurich dedicated to displaying posters. Finally, within the predicted cluster, the poster with the best proximity compared to the previous poster is selected. The mean square distance between features of posters was used to compute the proximity. To validate the predictive model, we compared sequences of 15 posters produced by our model to randomly and manually generated sequences. Manual sequences were created by a professional graphic designer. We asked 21 participants working as professional graphic designers to sort the sequences from the one with the strongest graphic line to the one with the weakest and to motivate their answer with a short description. The sequences produced by the designer were ranked first 60%, second 25% and third 15% of the time. The sequences produced by our predictive model were ranked first 25%, second 45% and third 30% of the time. The sequences produced randomly were ranked first 15%, second 29%, and third 55% of the time. Compared to designer sequences, and as reported by participants, model and random sequences lacked thematic continuity. According to the results, the proposed model is able to generate better poster sequencing compared to random sampling. Eventually, our algorithm is sometimes able to outperform a professional designer. As a next step, the proposed algorithm should include a possibility to create sequences according to a selected theme. To conclude, this work shows the potentiality of artificial intelligence techniques to learn from existing content and provide a tool to curate large sets of data, with a permanent renewal of the presented content.

Keywords: Artificial Intelligence, Digital Humanities, serendipity, design research

Procedia PDF Downloads 184

Authors: Diana Mitova, Krassimir Mitrev

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To prepare the current generation for the future, education systems need to change. It implies a way of learning that meets the demands of the times and the environment in which we live. Productive interaction in the educational process implies an interactive learning environment and the possibility of personal development of learners based on communication and mutual dialogue, cooperation and good partnership in decision-making. Students need not only theoretical knowledge, but transferable skills that will help them to become inventors and entrepreneurs, to implement ideas. STEM education , is now a real necessity for the modern school. Through learning in a "learning company", students master examples from classroom practice, simulate real life situations, group activities and apply basic interactive learning strategies and techniques. The learning company is the subject of this study, reduced to entrepreneurship training in STEM - technologies that encourage students to think outside the traditional box. STEM learning focuses the teacher's efforts on modeling entrepreneurial thinking and behavior in students and helping them solve problems in the world of business and entrepreneurship. Learning based on the implementation of various STEM projects in extracurricular activities, experiential learning, and an interdisciplinary approach are means by which educators better connect the local community and private businesses. Learners learn to be creative, experiment and take risks and work in teams - the leading characteristics of any innovator and future entrepreneur. This article presents some European policies on STEM and entrepreneurship education. It also shares best practices for training company training , with the integration of STEM in the learning company training environment. The main results boil down to identifying some advantages and problems in STEM entrepreneurship education. The benefits of using integrative approaches to teach STEM within a training company are identified, as well as the positive effects of project-based learning in a training company using STEM. Best practices for teaching entrepreneurship through extracurricular activities using STEM within a training company are shared. The following research methods are applied in this research paper: Theoretical and comparative analysis of principles and policies of European Union countries and Bulgaria in the field of entrepreneurship education through a training company. Experiences in entrepreneurship education through extracurricular activities with STEM application within a training company are shared. A questionnaire survey to investigate the motivation of secondary vocational school students to learn entrepreneurship through a training company and their readiness to start their own business after completing their education. Within the framework of learning through a "learning company" with the integration of STEM, the activity of the teacher-facilitator includes the methods: counseling, supervising and advising students during work. The expectation is that students acquire the key competence "initiative and entrepreneurship" and that the cooperation between the vocational education system and the business in Bulgaria is more effective.

Keywords: STEM, entrepreneurship, training company, extracurricular activities

Procedia PDF Downloads 96

Authors: Theresia El Khoury, Georges Badr, Amir Hajjam El Hassani, Stéphane N’Guyen Van Ky

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The widespread adoption of machine learning and artificial intelligence across different domains can be attributed to the digitization of data over several decades, resulting in vast amounts of data, types, and structures. Thus, data processing and preparation turn out to be a crucial stage. However, applying these techniques to S1000D standard-based data poses a challenge due to its complexity and the need to preserve logical information. This paper describes SALERNO, an S1000d AppLicability dEpended pRocessiNg mOdel. This python-based model analyzes and converts the XML S1000D-based files into an easier data format that can be used in machine learning techniques while preserving the different logic and relationships in files. The model parses the files in the given folder, filters them, and extracts the required information to be saved in appropriate data frames and Excel sheets. Its main idea is to group the extracted information by applicability. In addition, it extracts the full text by replacing internal and external references while maintaining the relationships between files, as well as the necessary requirements. The resulting files can then be saved in databases and used in different models. Documents in both English and French languages were tested, and special characters were decoded. Updates on the technical manuals were taken into consideration as well. The model was tested on different versions of the S1000D, and the results demonstrated its ability to effectively handle the applicability, requirements, references, and relationships across all files and on different levels.

Keywords: aeronautics, big data, data processing, machine learning, S1000D

Procedia PDF Downloads 157