Search results for: business intelligence and analytics

Authors: Benlakhdar Mohamed Larbi, Yagoub Asma

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Purpose: This study aims at highlighting the significance and importance of utilizing marketing information system (MKIS) on decision-making, by clarifying the need for quick and efficient decision-making due to time saving and preventing of duplication of work. Design, methodology, approach: The study shows the roles of each part of MKIS for developing marketing strategy, which present a real challenge to individuals and institutions in an era characterized by uncertainty and clarifying the importance of each part separately, depending on decision type and the nature of the situation. The empirical research method was evaluated by specialized experts, conducted by means of questionnaires. Correlation analysis was employed to test the validity of the procedure. Results: The empirical study findings confirmed positive relationships between the level of utilizing and adopting ‘decision support system and marketing intelligence’ and the success of an organizational decision-making, and provide the organization with a competitive advantage as it allows the organization to solve problems. Originality/value: The study offer better understanding of performance- increasing market share as an organizational decision making based on marketing information system.

Keywords: database, marketing research, marketing intelligence, decision support system, decision-making

Procedia PDF Downloads 315

Authors: Hsin Lee, Hsuan Lee

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Legal artificial intelligence (LegalAI) has been increasing applications within legal systems, propelled by advancements in natural language processing (NLP). Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. Most existing language models have difficulty understanding the long-distance dependencies between different structures. Another unique challenge is that while the Judiciary of Taiwan has released legal judgments from various levels of courts over the years, there remains a significant obstacle in the lack of labeled datasets. This deficiency makes it difficult to train models with strong generalization capabilities, as well as accurately evaluate model performance. To date, models in Taiwan have yet to be specifically trained on judgment data. Given these challenges, this research proposes a Longformer-based pre-trained language model explicitly devised for retrieving similar judgments in Taiwanese legal documents. This model is trained on a self-constructed dataset, which this research has independently labeled to measure judgment similarities, thereby addressing a void left by the lack of an existing labeled dataset for Taiwanese judgments. This research adopts strategies such as early stopping and gradient clipping to prevent overfitting and manage gradient explosion, respectively, thereby enhancing the model's performance. The model in this research is evaluated using both the dataset and the Average Entropy of Offense-charged Clustering (AEOC) metric, which utilizes the notion of similar case scenarios within the same type of legal cases. Our experimental results illustrate our model's significant advancements in handling similarity comparisons within extensive legal judgments. By enabling more efficient retrieval and analysis of legal case documents, our model holds the potential to facilitate legal research, aid legal decision-making, and contribute to the further development of LegalAI in Taiwan.

Keywords: legal artificial intelligence, computation and language, language model, Taiwanese legal cases

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Authors: Hanadi Mubarak Al-Mubaraki, Michael Busler

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Innovation is the powerful tools for economic growth and diversification, which lead to smart growth. The objective of this paper is to identify the opportunities and challenges of innovation programs discuss and analyse the implementation of the innovation program in the United States (US) and United Kingdom (UK). To achieve the objectives, the research used a mixed methods approach, quantitative (survey), and qualitative (multi-case study) to examine innovation best practices in developed countries. In addition, the selection of 4 interview case studies of innovation organisations based on the best practices and successful implementation worldwide. The research findings indicated the two challenges such as 1) innovation required business ecosystem support to deliver innovation outcomes such as new product and new services, and 2) foster the climate of innovation &entrepreneurship for economic growth and diversification. Although the two opportunities such as 1) sustainability of the innovation events which lead smart growth, and 2) establish the for fostering the artificial intelligence hub entrepreneurship networking at multi-levels. The research adds value to academicians and practitioners such as government, funded organizations, institutions, and policymakers. The authors aim to conduct future research a comparative study of innovation case studies between developed and developing countries for policy implications worldwide. The Originality of This study contributes to current literature about the innovation best practice in developed and developing countries.

Keywords: economic development, technology transfer, entrepreneurship, innovation program

Procedia PDF Downloads 136

Authors: Amreth Chandrasehar

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Machine Learning (ML) Models are developed and run in production to solve various use cases that help organizations to be more efficient and help drive the business. But this comes at a massive development cost and lost business opportunities. According to the Gartner report, 85% of data science projects fail, and one of the factors impacting this is not paying attention to Model Observability. Model Observability helps the developers and operators to pinpoint the model performance issues data drift and help identify root cause of issues. This paper focuses on providing insights into incorporating model observability in model development and operationalizing it in production.

Keywords: model observability, monitoring, drift detection, ML observability platform

Procedia PDF Downloads 94

Authors: Noor Azwa Azreen Binti Abd. Aziz, Muhamad Zaim Bin Mohd Rozi

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Moving towards the 4th Industrial Revolution, artificial intelligence (AI) is now more popular than ever. This subject is gaining significance every day and is continually expanding, often merging with other fields. Instead of merely being passive observers, there are benefits to understanding modern technology by delving into its inner workings. However, in a world teeming with digital information, the impact of AI on the spread of disinformation has garnered significant attention. The dissemination of inaccurate or misleading information is referred to as misinformation, posing a serious threat to democratic society, public debate, and individual decision-making. This article delves deep into the connection between AI and the dissemination of false information, exploring its potential, risks, and ethical issues as AI technology advances. The rise of AI has ushered in a new era in the dissemination of misinformation as AI-driven technologies are increasingly responsible for curating, recommending, and amplifying information on online platforms. While AI holds the potential to enhance the detection and mitigation of misinformation through natural language processing and machine learning, it also raises concerns about the amplification and propagation of false information. AI-powered deepfake technology, for instance, can generate hyper-realistic videos and audio recordings, making it increasingly challenging to discern fact from fiction.

Keywords: artificial intelligence, digital information, disinformation, ethical issues, misinformation

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Authors: Mrigakshi Das

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The ethics of a business is currently a high profile issue owing to sensational corporate scandals that had taken place in many countries causing extensive damages to the economy and society. These corporate scandals question the morality of businessmen in general and accountants in particular. It is argued that the accountants have been the main contributors to the decline in ethical standards of a business. This researcher has reviewed the need and impact of ethics education in accounting profession. Despite of ethical interventions, the rate of accounting scandals are increasing and have left the public questioning that has the profession become really less ethical?

Keywords: accounting, ethics education and intervention in accounting, accounting education, accounting profession, moral reasoning and development, ethics education

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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: Jingya Liu, Yue Wu, Jiabin Luo

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This work investigates an intermodal transportation system for delivering goods from a Regional Distribution Centre to supermarkets on the Isle of Wight (IOW) via the port of Southampton or Portsmouth in the UK. We consider this integrated logistics chain as a 3-echelon transportation system. In such a system, there are two types of transport methods used to deliver goods across the Solent Channel: one is accompanied transport, which is used by most supermarkets on the IOW, such as Spar, Lidl and Co-operative food; the other is unaccompanied transport, which is used by Aldi. Five transport scenarios are studied based on different transport modes and ferry routes. The aim is to determine an optimal delivery plan for supermarkets of different business scales on IOW, in order to minimise the total running cost, fuel consumptions and carbon emissions. The problem is modelled as a vehicle routing problem with time windows and solved by genetic algorithm. The computing results suggested that accompanied transport is more cost efficient for small and medium business-scale supermarket chains on IOW, while unaccompanied transport has the potential to improve the efficiency and effectiveness of large business scale supermarket chains.

Keywords: genetic algorithm, intermodal transport system, Isle of Wight, optimization, supermarket

Procedia PDF Downloads 361

Authors: Subhadeep Mandal, Sujoy Bhattacharya, Pabitra Mitra, Diya Guha Roy, Seema Bhattacharya

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E-commerce firms allow customers to evaluate and review the things they buy as a positive or bad experience. The e-commerce transaction processes are made up of a variety of diverse organizations and activities that operate independently but are connected together to complete the transaction (from placing an order to the goods reaching the client). After a negative shopping experience, clients frequently disregard the critical assessment of these businesses and submit their feedback on an all-over basis, which benefits certain enterprises but is tedious for others. In this article, we solely dealt with negative reviews and attempted to distinguish between negative reviews where the e-commerce firm is explicitly blamed by customers for a bad purchasing experience and other negative reviews.

Keywords: e-commerce, online shopping, customer reviews, customer behaviour, text analytics, n-grams classification

Procedia PDF Downloads 248

Authors: Masomeh Jamshid Nejad

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Nowadays, statistical literacy is no longer a necessary skill but an essential skill with broad applications across diverse fields, especially in operational decision areas such as business management, finance, and economics. As such, learning and deep understanding of statistical concepts are essential in the context of business studies. One of the crucial topics in statistical theory and its application is the normal distribution, often called a bell-shaped curve. To interpret data and conduct hypothesis tests, comprehending the properties of normal distribution (the mean and standard deviation) is essential for business students. This requires undergraduate students in the field of economics and business management to visualize and work with data following a normal distribution. Since technology is interconnected with education these days, it is important to teach statistics topics in the context of Python, R-studio, and Microsoft Excel to undergraduate students. This research endeavours to shed light on the effect of Excel-based instruction on learners’ knowledge of statistics, specifically the central concept of normal distribution. As such, two groups of undergraduate students (from the Business Management program) were compared in this research study. One group underwent Excel-based instruction and another group relied only on traditional teaching methods. We analyzed experiential data and BBA participants’ responses to statistic-related questions focusing on the normal distribution, including its key attributes, such as the mean and standard deviation. The results of our study indicate that exposing students to Excel-based learning supports learners in comprehending statistical concepts more effectively compared with the other group of learners (teaching with the traditional method). In addition, students in the context of Excel-based instruction showed ability in picturing and interpreting data concentrated on normal distribution.

Keywords: statistics, excel-based instruction, data visualization, pedagogy

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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: Jocelyn B. Barbosa, Angeli L. Magbaril, Mariel T. Sabanal, John Paul T. Galario, Mikka P. Baldovino

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The use of technology has become ubiquitous in different areas of business today. With the advent of digital imaging and database technology, business owners have been motivated to integrate technology to their business operation ranging from small, medium to large enterprises. Technology has been found to have brought many benefits that can make a business grow. Hog or swine raising, for example, is a very popular enterprise in the Philippines, whose challenges in production monitoring can be addressed through technology integration. Swine production monitoring can become a tedious task as the enterprise goes larger. Specifically, problems like delayed and inconsistent reports are most likely to happen if counting of swine per pen of which building is done manually. In this study, we present iCount, which aims to ensure efficient swine detection and counting that hastens the swine production monitoring task. We develop a system that automatically detects and counts swine based on Sobel filter and ellipse fitting model, given the still photos of the group of swine captured in a pen. We improve the Sobel filter detection result through 8-neigbhorhood rule implementation. Ellipse fitting technique is then employed for proper swine detection. Furthermore, the system can generate periodic production reports and can identify the specific consumables to be served to the swine according to schedules. Experiments reveal that our algorithm provides an efficient way for detecting swine, thereby providing a significant amount of accuracy in production monitoring.

Keywords: automatic swine counting, swine detection, swine production monitoring, ellipse fitting model, sobel filter

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Authors: Valencia P. L. Rodrigo, Morita A. Adelina, Vargas V. Martin

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This proposal consists of an analysis of macro concepts involved within an organization growth using educational technologies, which will relate each concept, in a mathematical way with a vision of harmonic work. Working collaboratively, competitively and cooperatively so that this growth is harmonious and homogenous, coining a new term, Harmonic Work. The Harmonic Work ensures that the organization grows in all business directions, allowing managers to project a much more accurate growth, making clear the contribution of each department, resulting in an algorithm that analyzes each of the variables both endogenous and exogenous, establishing different performance indicators in its process of growth.

Keywords: business projection, collaboration, competitiveness, educational technology, harmonious growth

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Authors: B. Seetanah, R. V. Sannassee, Lamport, K. Padachi, K. Seetah, S. Matadeen, N. Okurutt, N. Ama, L. Mokoodi

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This paper analyses the impact of the various enabling elements towards fostering entrepreneurial behavior for two Sub Saharan African countries namely Mauritius and Botswana, with focus is on role of institutions (ministries, government support institutions, financing institutions and SME associations). Using a structural equation modeling framework, it is found that finance was some of the most determinant of respondents’ evaluation of the business climate thus emphasizing on the crucial of such an ingredient. Interestingly government related factors such as government support and institutional support are also reported to have a significant influence on the SME business climate in both countries.

Keywords: institutions, SME, SEM, Mauritius, Botswana

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Authors: Olanrewaju Johnson Akinduntire

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This paper investigates the statistical evaluation of books, citations, articles, journals and other publications in accounting and finance on leveraging technology to mitigate the impact of COVID-19 on business. The research proffers an appraisal of the impact of computerized accounting systems in pre and post pandemic era on activities of the formal and informal sectors, it analyzes the concept of computerized accounting systems, and it seeks to determine the impact of computerized of the overall activities of the informal sector. A special focus of this ICT strategy should be to demystify and promote the diffusion of ICT as a general-purpose technology to the informal sector. It is believed that the use of new technologies can be crucial to meeting the Millennium Development Goals (MDGs) in a timely and effective fashion. Consequent to these, there is a need to prevent the further marginalization of the informal sector by availing ICT services which are mixed appropriately and also properly located. By implication, this will help them access markets and other business information, which can enable or make their economic activities more vibrant and facilitate the availability of information about new opportunities. Conclusively, for one to understand the application of ICT and their locational dynamics in informal sector clusters, there is a need to comprehend and acknowledge the drivers and pressures leading to the adoption of new technology.

Keywords: COVID-19 , (MDGs) , ICT, bibliometric

Procedia PDF Downloads 173

Authors: Nancy Ayman Kamal Mohamed Mehrz

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As the economies of other countries in the Mediterranean Basin, the tourism sector in our country has a high denominator in economics. Tourism businesses, which are building blocks of tourism, sector faces with a variety of problems during their activities. These problems faced make business efficiency and competition conditions of the businesses difficult. Most of the problems faced by the tourism businesses and the information of consumers about consumers’ rights were used in this study, which is conducted to determine the problems of tourism businesses in the Central Anatolia Region. It is aimed to contribute the awareness of staff and executives working at tourism sector and to attract attention of businesses active concurrently with tourism sector and legislators. E-tourism is among the issues that have recently been entered into the field of tourism. In order to achieve this type of tourism, Information and Communications Technology (or ICT) infrastructures as well as Co-governmental organizations and tourism resources are important. In this study, the opinions of managers and tourism officials about the e-tourism in Leman city were measured; it also surveyed the impact of level of digital literacy of managers and tourism officials on attracting tourists. This study was conducted. One of the environs of the Esfahan province. This study is a documentary – survey and the sources include library resources and also questionnaires. The results obtained indicate that if managers use ICT, it may help e-tourism to be developed in the region, and increasing managers’ beliefs on e-tourism and upgrading their level of digital literacy may affect e-tourism development.

Keywords: financial problems, the problems of tourism businesses, tourism businesses, internet, marketing, tourism, tourism management economic competitiveness, enhancing competitiveness

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Authors: Jamuna Loganath

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Education is the key to the development of any individual’s personality. Today’s students will be tomorrow’s citizens of the global society. The education of the student is the edifice on which his/her future will be built. Schools therefore should provide an all-round development of students so as to foster a healthy society. The behaviors and the attitude of the students in school play an essential role for the success of the education process. Frequent reports of misbehaviors such as clowning, harassing classmates, verbal insults are becoming common in schools today. If this issue is left unattended, it may develop a negative attitude and increase the delinquent behavior. So, the need of the hour is to find a solution to this problem. To solve this issue, it is important to monitor the students’ behaviors in school and give necessary feedback and mentor them to develop a positive attitude and help them to become a successful grownup. Nevertheless, measuring students’ behavior and attitude is extremely challenging. None of the present technology has proven to be effective in this measurement process because actions, reactions, interactions, response of the students are rarely used in the course of the data due to complexity. The purpose of this proposal is to recommend an effective supervising system after carrying out a feasibility study by measuring the behavior of the Students. This can be achieved by equipping schools with CCTV cameras. These CCTV cameras installed in various schools of the world capture the facial expressions and interactions of the students inside and outside their classroom. The real time raw videos captured from the CCTV can be uploaded to the cloud with the help of a network. The video feeds get scooped into various nodes in the same rack or on the different racks in the same cluster in Hadoop HDFS. The video feeds are converted into small frames and analyzed using various Pattern recognition algorithms and MapReduce algorithm. Then, the video frames are compared with the bench marking database (good behavior). When misbehavior is detected, an alert message can be sent to the counseling department which helps them in mentoring the students. This will help in improving the effectiveness of the education process. As Video feeds come from multiple geographical areas (schools from different parts of the world), BIG DATA helps in real time analysis as it analyzes computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. It also analyzes data that can’t be analyzed by traditional software applications such as RDBMS, OODBMS. It has also proven successful in handling human reactions with ease. Therefore, BIG DATA could certainly play a vital role in handling this issue. Thus, effectiveness of the education process can be enhanced with the help of video analytics using the latest BIG DATA technology.

Keywords: big data, cloud, CCTV, education process

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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: Reuben Govender, M. L. Woermann

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The ISO 14001 management system standard was first published in 1996. It is a voluntary standard adopted by both private and public sector organizations globally. Adoption of the ISO 14001 standard at the corporate level is done to help manage business impacts on the environment e.g. pollution control. The International Organization for Standardization (ISO) revised the standard in 2004 and recently in 2015. The current revision of the standard appears to adopt a communitarian-type philosophy. The inclusion of requirements to consider external 'interested party' needs and expectations implies this philosophy. Therefore, at operational level businesses implementing ISO 14001 will have to consider needs and expectations beyond local laws. Should these external needs and expectations be included in the scope of the environmental management system, they become requirements to be complied with in much the same way as compliance to laws. The authors assert that the recent changes to ISO 14001 introduce an ethical dimension to the standard. The authors assert that business ethics as a discipline now finds relevance in ISO 14001 via contemporary stakeholder theory and discourse ethics. Finally, the authors postulate implications of (not) addressing these requirements before July 2018 when transition to the revised standard must be complete globally.

Keywords: business ethics, environmental ethics, ethics practice, ISO 14001:2015

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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: Azman Ramlie, Vivian Foo Jing Wen

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As the advancement of technology has tremendously changed the way people communicate with each other, it has opened up opportunities for interracial relationships. Interracial relationship is also known as intercultural or interethnic relationships. This research topic aims to study on students’ perceptions toward interracial relationship in terms of their level of acceptance and approval. In addition, the study also focuses on determining and ranking the factors that affects students’ perceptions towards interracial relationship. This study also targets to determine the differences in students’ perceptions from business and communication major. This study uses a sample of university students from Sunway University, particularly on students in business and communication major. The study was conducted through online survey questionnaires. Results revealed that students’ level of acceptance towards interracial relationship was high. In addition, results also further indicated that family members’ support was one of the most important factors in influencing students’ perception towards interracial relationship. No diverse differences of perceptions among students in business and communication major towards interracial relationship were shown in the findings. Most of the result showed that both majors turned out to have similar perceptions towards interracial relationship. It can be said that the students’ perception towards interracial relationship did not differ from which major the students was in but their family background that would shape their perceptions.

Keywords: interracial relationship, racial, relationship, perceptions

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Authors: Airy Huang, Fuji Foo, Aries Prasetya Wibowo

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In recent years, the focus of Natural Language Processing (NLP) development has been gradually shifting from the semantics-based approach to deep learning one, which performs faster with fewer resources. Although it performs well in many applications, the deep learning approach, due to the lack of semantics understanding, has difficulties in noticing and expressing a novel business case with a pre-defined scope. In order to meet the requirements of specific robotic services, deep learning approach is very labor-intensive and time consuming. It is very difficult to improve the capabilities of conversational AI in a short time, and it is even more difficult to self-learn from experiences to deliver the same service in a better way. In this paper, we present an adaptive conversational AI algorithm that combines both semantic knowledge and deep learning to address this issue by learning new business cases through conversations. After self-learning from experience, the robot adapts to the business cases originally out of scope. The idea is to build new or extended robotic services in a systematic and fast-training manner with self-configured programs and constructed dialog flows. For every cycle in which a chat bot (conversational AI) delivers a given set of business cases, it is trapped to self-measure its performance and rethink every unknown dialog flows to improve the service by retraining with those new business cases. If the training process reaches a bottleneck and incurs some difficulties, human personnel will be informed of further instructions. He or she may retrain the chat bot with newly configured programs, or new dialog flows for new services. One approach employs semantics analysis to learn the dialogues for new business cases and then establish the necessary ontology for the new service. With the newly learned programs, it completes the understanding of the reaction behavior and finally uses dialog flows to connect all the understanding results and programs, achieving the goal of self-learning process. We have developed a chat bot service mounted on a kiosk, with a camera for facial recognition and a directional microphone array for voice capture. The chat bot serves as a concierge with polite conversation for visitors. As a proof of concept. We have demonstrated to complete 90% of reception services with limited self-learning capability.

Keywords: conversational AI, chatbot, dialog management, semantic analysis

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Authors: Oyedele Ogundana, Amon Simba, Kostas Galanakis, Lynn Oxborrow

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The debate about factors influencing the business growth of women-owned businesses has been a topical issue in business management. Currently, scholars have identified the issues of access to money, market, and management as canvasing factors influencing the business growth of women-owned businesses. However, the influence of motherhood (household/family context) on business growth is inconclusive in the literature; despite that women are more family-oriented than their male counterparts. Therefore, this research study considers the influence of motherhood factor (household/family context) on the business growth of women-owned sewing businesses (WOSBs) in Lagos, Nigeria. The sewing business sector is chosen as the fashion industry (which includes sewing businesses) currently accounts for the second largest number of jobs in Sub-Saharan Africa, following agriculture. Thus, sewing businesses provide a rich ground for contributing to existing scholarly work. Research questions; (1) In what way does the motherhood factor influence the business growth of WOSBs in Lagos? (2) To what extent does the motherhood factor influence the business growth of WOSBs in Lagos? For the method design, a pragmatic approach, a mixed-methods technique and an abductive form of reasoning are adopted. The method design is chosen because it fits, better than other research perspectives, with the research questions posed in this study. For instance, using a positivist approach will not sufficiently answer research question 1, neither will an interpretive approach sufficiently answer research question 2. Therefore, the research method design is divided into 2 phases, and the results from one phase are used to inform the development of the subsequent phases (only phase 1 has been completed at the moment). The first phase uses qualitative data and analytical method to answer research question 1. While the second phase of the research uses quantitative data and analytical method to answer research question 2. For the qualitative phase, 5 WOSBs were purposefully selected and interviewed. The sampling technique is selected as it was not the intention of the researcher to make any statistical inferences, at this phase, rather the purpose was just exploratory. Therefore, the 5 sampled women comprised of 2 unmarried women, 1 married woman with no child, and 2 married women with children. A 40-60 minutes interview was conducted per participants. The interviews were audio-recorded and transcribed. Thereafter, the data were analysed using thematic analysis in order to unearth patterns and relationships. Findings for the first phase of this research reveals that motherhood (household/family context) directly influences (positively/negatively) the performance of WOSBs in Lagos. Apart from a direct influence on WOSBs, motherhood also moderates (positively/negatively) other factors–e.g., access to money, management/human resources and market/opportunities– influencing WOSBs in Lagos. To further strengthen this conclusion, a word frequency query result shows that ‘family,’ ‘husband’ and ‘children’ are among the 10 words used frequently in all the interview transcripts. This first phase contributes to existing studies by showing the various forms by which motherhood influences WOSBs. The second phase (which data are yet to be collected) would reveal the extent to which motherhood influence the business growth of WOSBs in Lagos.

Keywords: women-owned sewing businesses, business growth, motherhood, Lagos

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Authors: Adam Bereczk

Abstract:

Due to persistent socioeconomic problems regarding sustainability and labour market equilibrium in Europe, the subjects of social economy gained considerable academic attention recently. At the meantime, social enterprises pursuing the double bottom line criteria, struggling to find the proper management philosophies and strategies to make their social purpose business financially sustainable. Despite the strategic management literature was developed mainly on the bases of large corporations, in the past years, the interpretation of strategy concepts became a frequent topic in scientific discussions in the case of small and medium-sized enterprises also. The topic of strategic orientations is a good example of the trend. However, less is known about the case of social enterprises, despite the fact, the majority of them are small businesses engaged in real business activities. The main purpose of this work is to give a comprehensive summary of different perspectives regarding the interpretations of strategic orientations of social enterprises. The novelty of this work is it shows the previous outcomes and models of scholars from various fields of economic science who tried to intertwine the two spheres in different forms, methodize the findings and draw attention to the shortcomings.

Keywords: social enterprises, business sustainability, strategic orientations, literature review

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Authors: Ana Hol

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Study and work operations are being transformed with the uses of technologies and are consequently becoming global. This paper outlines lessons learned based on the international study tour that Australian Bachelor of Information Systems students undertook. This research identifies that for the study tour to be successful, students need to gain skills that global citizens require. For example, students will need to gain an understanding of local cultures, local customs and habits. Furthermore, students would also need to gain an understanding of how a field of their future career expertise operates in the host country, how study and business are conducted internationally, which tools and technologies are currently being utilized on a global scale, what trends drive future developments world-wide and how business negotiations and collaborations are being undertaken across borders. Furthermore, this research provides a guide to educators who are planning, guiding and running study tours as it outlines the requirements of having a pre-tour preparatory session, carefully planned and executed tour itineraries and post-tour sessions during which students can reflect on their experiences and lessons learned so that they can apply them to future international business visits and ventures.

Keywords: global education, international experiences, international study tours, students as global citizens, student centered education,

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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: Robin Molinier

Abstract:

Industrial symbiosis (I.S) rely on two modes of cooperation that are infrastructure sharing and resource substitution to obtain economic and environmental benefits. The former consists in the intensification of use of an asset while the latter is based on the use of waste, fatal energy (and utilities) as alternatives to standard inputs. Both modes, in fact, rely on the shift from a business-as-usual functioning towards an alternative production system structure so that in a business point of view the distinction is not clear. In order to investigate the way those cooperation modes can be distinguished, we consider the stakeholders' interplay in the business model structure regarding their resources and requirements. For infrastructure sharing (following economic engineering literature) the cost function of capacity induces economies of scale so that demand pooling reduces global expanses. Grassroot investment sizing decision and the ex-post pricing strongly depends on the design optimization phase for capacity sizing whereas ex-post operational cost sharing minimizing budgets are less dependent upon production rates. Value is then mainly design driven. For resource substitution, synergies value stems from availability and is at risk regarding both supplier and user load profiles and market prices of the standard input. Baseline input purchasing cost reduction is thus more driven by the operational phase of the symbiosis and must be analyzed within the whole sourcing policy (including diversification strategies and expensive back-up replacement). Moreover, while resource substitution involves a chain of intermediate processors to match quality requirements, the infrastructure model relies on a single operator whose competencies allow to produce non-rival goods. Transaction costs appear higher in resource substitution synergies due to the high level of customization which induces asset specificity, and non-homogeneity following transaction costs economics arguments.

Keywords: business model, capacity, sourcing, synergies

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Authors: Georgios Pardalis, Krushna Mahapatra, Brijesh Mainali

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Compared to other sectors, the residential and service sector in Sweden is responsible for almost 40% of the national final energy use and faces great challenges towards achieving reduction of energy intensity. The one- and two-family (henceforth 'detached') houses, constituting 60% of the residential floor area and using 32 TWh for space heating and hot water purposes, offers significant opportunities for improved energy efficiency. More than 80% of those houses are more than 35 years of old and a large share of them need major renovations. However, the rate of energy renovations for such houses is significantly low. The renovation market is dominated by small and medium-sized local companies (SMEs), who mostly offer individual solutions. A one-stop-shop business framework, where a single actor collaborates with other actors and coordinates them to offer a full package for holistic renovations, may speed up the rate of renovation. Such models are emerging in some European countries. This paper aims to understand the willingness of the SMEs to adopt a one-stop-shop business framework. Interviews were conducted with 13 SMEs in Kronoberg county in Sweden, a geographic region known for its initiatives towards sustainability and energy efficiency. The examined firms seem reluctant to adopt one-stop-shop for nonce due to the perceived risks they see in such a business move and due to their characteristics, although they agree that such a move will advance their position in the market and their business volume. By using threat-rigidity and prospect theory, we illustrate how this type of companies can move from being reluctant to adopt one-stop-shop framework to its adoption. Additionally, with the use of behavioral theory, we gain deeper knowledge on those exact reasons preventing those firms from adopting the one-stop-shop framework.

Keywords: construction SMEs, innovation adoption, one-stop-shop, perceived risks

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Authors: Thor Alexis Sazon, Alexander Guzman-Urbina, Yasuhiro Fukushima

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This study outlines the method of how to develop a surrogate life cycle model based on fuzzy logic using three fuzzy inference methods: (1) the conventional Fuzzy Inference System (FIS), (2) the hybrid system of Data Analytics and Fuzzy Inference (DAFIS), which uses data clustering for defining the membership functions, and (3) the Adaptive-Neuro Fuzzy Inference System (ANFIS), a combination of fuzzy inference and artificial neural network. These methods were demonstrated with a case study where the Global Warming Potential (GWP) and the Levelized Cost of Energy (LCOE) of solar photovoltaic (PV) were estimated using Solar Irradiation, Module Efficiency, and Performance Ratio as inputs. The effects of using different fuzzy inference types, either Sugeno- or Mamdani-type, and of changing the number of input membership functions to the error between the calibration data and the model-generated outputs were also illustrated. The solution spaces of the three methods were consequently examined with a sensitivity analysis. ANFIS exhibited the lowest error while DAFIS gave slightly lower errors compared to FIS. Increasing the number of input membership functions helped with error reduction in some cases but, at times, resulted in the opposite. Sugeno-type models gave errors that are slightly lower than those of the Mamdani-type. While ANFIS is superior in terms of error minimization, it could generate solutions that are questionable, i.e. the negative GWP values of the Solar PV system when the inputs were all at the upper end of their range. This shows that the applicability of the ANFIS models highly depends on the range of cases at which it was calibrated. FIS and DAFIS generated more intuitive trends in the sensitivity runs. DAFIS demonstrated an optimal design point wherein increasing the input values does not improve the GWP and LCOE anymore. In the absence of data that could be used for calibration, conventional FIS presents a knowledge-based model that could be used for prediction. In the PV case study, conventional FIS generated errors that are just slightly higher than those of DAFIS. The inherent complexity of a Life Cycle study often hinders its widespread use in the industry and policy-making sectors. While the methodology does not guarantee a more accurate result compared to those generated by the Life Cycle Methodology, it does provide a relatively simpler way of generating knowledge- and data-based estimates that could be used during the initial design of a system.

Keywords: solar photovoltaic, fuzzy logic, inference system, artificial neural networks

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