Search results for: customer friendly washing machine

Authors: M. Kamal, Y. Wang, R. Kennon

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Quality Function Deployment is a useful tool in product development with the application of voice of customer. In this paper, it aims to be applied as a product development tool in redesigning fashion and textile product. The purpose of these studies is to apply the effective use of Voice of Customer in redesigning cultural fashion product. The data collection from Voice of Customer or consumers’ feedback might help the producer to improve the quality of merchandise ahead. Voice of Customer could give a specific detailing for quality which needs to be redesigned according to customers’ requirements. Meanwhile, the next objective is to differentiate design specifications and characteristics using House of Quality. In product designing phase, it is very important to distinguish each specification and characteristic which translated from Voice of Customer to House of Quality matrix. This matrix would help designers to development according to qualities that customer wants for the better and successful product in the market. It is hope this research would indicate the customers’ requirements and production team idea might be measured and translated to a systematic data. The specific technical data could be planned ahead with specific design details as well. This could be a sustainable approach for a traditional product which could control the material that they use and sustain the quality as the past production. As a conclusion, this study would benefit the Small Medium Enterprises design team or the designers to style an item from customers view with organised projection of the product. The finding also could assist designers or batik producers’ to recognise specific details Batik sarong from consumers as well as in in advertising and marketing strategy plan.

Keywords: house of quality, Malaysia batik sarong, quality function deployment, voice of customer

Procedia PDF Downloads 593

Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modelling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: sentiment analysis, social media, Twitter, Amazon, data mining, machine learning, text mining

Procedia PDF Downloads 353

Authors: Giuseppina Settanni, Antonio Panarese, Raffaele Vaira, Maurizio Galiano

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Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it.

Keywords: machine learning, recommender system, software platform, support vector machine

Procedia PDF Downloads 134

Authors: Adnan Gohar

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This research work aims the importance of internal employees in the making of a brand (cell phone) through customer satisfaction which basically explains the connection of internal employees with external customers. This research is designed to measure the satisfaction level of internal employees which further connects to the product evolution as a brand leaving a brand image in the eye of the external customer. The main focus is that internal employees are as important as external customers for the uplift of the product resulting in the brand. Internal employees are individual organization employees, vendors, departments, and distributors.

Keywords: brand building, customer satisfaction, internal employees, mobile franchise

Procedia PDF Downloads 257

Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

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Authors: Mahshid Asadollahi, Mohammad Akbari Asl

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The restaurant industry is highly competitive, and restaurants are constantly looking for ways to attract new customers and retain their existing ones. Nonverbal communication is an important factor in creating a positive customer experience and can play a significant role in attracting customers to restaurants. Nonverbal communication can include body language, facial expressions, tone of voice, and physical proximity, among other things. The present study aimed to investigate the impact of nonverbal communication between restaurant employees and customers on attracting customers in food courts in Tehran. The research method was descriptive-correlational, and the statistical population of this study included all customers of food court restaurants in Tehran, which was about 30 restaurants. The research sample was selected through probability sampling, and 440 customers completed emotional response, customer satisfaction, and nonverbal communication questionnaires in person. The data obtained were analyzed using multiple regression analysis. The results showed that vocal language, employee proximity, physical appearance, and speech movements, as components of nonverbal communication of restaurant employees, had an impact on attracting customers. Additionally, positive and negative emotions of customers have a significant relationship with customer attraction in Food Court restaurants. The study shows that various nonverbal communication factors can play a significant role in attracting customers, and that positive and negative customer emotions can affect customer satisfaction. Therefore, restaurant owners and managers should pay attention to nonverbal communication and train their employees accordingly to create a positive and welcoming atmosphere for customers.

Keywords: verbal language, proximity of employees, physical appearance, speech gestures, nonverbal communication, customer emotions, customer attraction

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Authors: Patrícia Branco, Catarina Prista, Helena Albergaria

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Industrial fuel-ethanol fermentations are carried out under non-sterile conditions, which favors the development of microbial contaminants, leading to huge economic losses. Wild yeasts such as Brettanomyces bruxellensis and lactic acid bacteria are the main contaminants of industrial bioethanol fermentation, affecting Saccharomyces cerevisiae performance and decreasing ethanol yields and productivity. In order to control microbial contaminations, the fuel-ethanol industry uses different treatments, including acid washing and antibiotics. However, these control measures carry environmental risks such as acid toxicity and the rise of antibiotic-resistant bacteria. Therefore, it is crucial to develop and apply less toxic and more environmentally friendly biocontrol methods. In the present study, an industrial fuel-ethanol starter, S. cerevisiae Ethanol-Red, was genetically modified to over-express AMPs with activity against fuel-ethanol microbial contaminants and evaluated regarding its biocontrol effect during mixed-culture alcoholic fermentations artificially contaminated with B. bruxellensis. To achieve this goal, S. cerevisiae Ethanol-Red strain was transformed with a plasmid containing the AMPs-codifying genes, i.e., partial sequences of TDH1 (925-963 bp) and TDH2/3 (925-963 bp) and a geneticin resistance marker. The biocontrol effect of those genetically modified strains was evaluated against B. bruxellensis and compared with the antagonistic effect exerted by the modified strain with an empty plasmid (without the AMPs-codifying genes) and the non-modified strain S. cerevisiae Ethanol-Red. For that purpose, mixed-culture alcoholic fermentations were performed in a synthetic must use the modified S. cerevisiae Ethanol-Red strains together with B. bruxellensis. Single-culture fermentations of B. bruxellensis strains were also performed as a negative control of the antagonistic effect exerted by S. cerevisiae strains. Results clearly showed an improved biocontrol effect of the genetically-modified strains against B. bruxellensis when compared with the modified Ethanol-Red strain with the empty plasmid (without the AMPs-codifying genes) and with the non-modified Ethanol-Red strain. In mixed-culture fermentation with the modified S. cerevisiae strain, B. bruxellensis culturability decreased from 5×104 CFU/mL on day-0 to less than 1 CFU/mL on day-10, while in single-culture B. bruxellensis increased its culturability from 6×104 to 1×106 CFU/mL in the first 6 days and kept this value until day-10. Besides, the modified Ethanol-Red strain exhibited an enhanced antagonistic effect against B. bruxellensis when compared with that induced by the non-modified Ethanol-Red strain. Indeed, culturability loss of B. bruxellensis after 10 days of fermentation with the modified Ethanol-Red strain was 98.7 and 100% higher than that occurred in fermentations performed with the non-modified Ethanol-Red and the empty-plasmid modified strain, respectively. Therefore, one can conclude that the S. cerevisiae genetically modified strain obtained in the present work may be a valuable solution for the mitigation of microbial contamination in fuel-ethanol fermentations, representing a much safer and environmentally friendly preservation strategy than the antimicrobial treatments (acid washing and antibiotics) currently applied in fuel-ethanol industry.

Keywords: antimicrobial peptides, fuel-ethanol microbial contaminations, fuel-ethanol fermentation, biocontrol agents, genetically-modified yeasts

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Authors: Aspalella A. Rahman

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It is widely accepted that the investigation of money laundering and the tracing and confiscation of criminal proceeds have intruded into the principles of banking secrecy. The inroads into banking secrecy present serious threats to democracy, and more importantly, to the traditional banker-customer relationship. It is generally accepted that the fight against money laundering is in conflict with the secrecy rule. Banking secrecy is a customer privilege whereas combating crime is critical for public safety and security. Indeed, achieving a proper balance is a desirable goal. But how we go about achieving such a balance is a question encountered by many law enforcement authorities. Therefore, this paper examines the effect of disclosure under the Malaysian anti-money laundering laws on the traditional duty of banks to keep the customer’s information confidential. It also analyzes whether the Malaysian laws provide a right balance between a duty to keep customer’s information secret and a duty to disclose such information in the fight against money laundering. On closer inspection, it is submitted that the Malaysian laws provide sufficient safeguards to ensure that the disclosure of customer’s information is carried out in a manner that is not prejudicial to the interest of legitimate customers. This is a positive approach that could protect the innocent customers from being mistreated by the law. Ultimately, it can be said that the growing threat of global money laundering and terrorism makes the overriding of banking secrecy justified because without a flow of information from the banks, the effective prevention of the menace is not possible.

Keywords: anti-money laundering law, banker-customer relationship, banking secrecy, confidentiality, money laundering

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Authors: Mauricio Quintero Angel, Andrés A. Duque Nivia, Carlos H. Fajardo Toro

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This paper analyzes two approaches of sustainability, the weak and strong, considering a case of study of oil palm production for an industry of biodegradable detergent. In this case, a company demand the oil palm as the active element for washing and through its trademark aims to supply 10% of the Colombian market of washing powders. Under each approach the economic and ecological implications of the palm oil production and especially the implications for crop management are described. The crop production under the weak sustainability implies plantations, intensive use of agrochemicals and the inclusion of new areas of cultivation as the market grows. Under the strong sustainability the production system is limited by the productive vocation of the ecosystem, so that new approaches and creativity for making viable the nature conservancy and the business development are require.

Keywords: agriculture, environmental impacts, oil palm, strong sustainability, weak sustainability

Procedia PDF Downloads 433

Authors: Monica Lia

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This article presents a customer data analysis model using business intelligence tools for data modelling, transforming, data visualization and dynamic reports building. Economic organizational customer’s analysis is made based on the information from the transactional systems of the organization. The paper presents how to develop the data model starting for the data that companies have inside their own operational systems. The owned data can be transformed into useful information about customers using business intelligence tool. For a mature market, knowing the information inside the data and making forecast for strategic decision become more important. Business Intelligence tools are used in business organization as support for decision-making.

Keywords: customer analysis, business intelligence, data warehouse, data mining, decisions, self-service reports, interactive visual analysis, and dynamic dashboards, use cases diagram, process modelling, logical data model, data mart, ETL, star schema, OLAP, data universes

Procedia PDF Downloads 434

Authors: Rajasekhar Mamilla, G. Janardhana, G. Anjan Babu

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The present research studies analyses the students’ satisfaction with university performance regarding the reliability dimension, ability of professors and staff to perform the promised services with quality to students in the post-graduate courses offered by Sri Venkateswara University in India. The research is done with the notion that the student compares the perceived performance with prior expectations. Customer satisfaction is seen as the outcome of this comparison. The sample respondents were administered with the schedule based on the stratified random technique for this study. Statistical techniques such as factor analysis, t-test and correlation analysis were used to accomplish the respective objectives of the study.

Keywords: satisfaction, reliability, service quality, customer

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Authors: A. B. Robert, Adam Pramadia, Calvin Andika

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The main purpose of this study is to explore consumer loyalty characteristic of mobile phone category in Indonesia. Second, this research attempts to identify consumer segment and to explore their profile in each segment as the basis of marketing strategy formulation. This study used some tools of multivariate analysis such as discriminant analysis and cluster analysis. Discriminate analysis used to discriminate consumer loyal and not loyal by using particular variables. Cluster analysis used to reveal various segment in mobile phone category. In addition to having better customer understanding in each segment, this study used descriptive analysis and cross tab analysis in each segment defined by cluster analysis. This study expected several findings. First, consumer can be divided into two large group of loyal versus not loyal by set of variables. Second, this study identifies customer segment in mobile phone category. Third, exploring customer profile in each segment that has been identified. This study answer a call for additional empirical research into different product categories. Therefore, a replication research is advisable. By knowing the customer loyalty characteristic, and deep analysis of their consumption behavior and profile for each segment, this study is very advisable for high impact marketing strategy development. This study contributes body of knowledge by adding empirical study of consumer loyalty, segmentation analysis in mobile phone category by multiple brand analysis.

Keywords: customer loyalty, segmentation, marketing strategy, discriminant analysis, cluster analysis, mobile phone

Procedia PDF Downloads 597

Authors: Mei-Fang Wu, Shu-Li Wang, Tsung-Yueh Lu, Feng-Tsung Cheng

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Strengthening core competitiveness is the main goal of enterprises in a fierce competitive environment. Accurate cost information is a great help for managers in dealing with operation strategies. This paper establishes a profitability management mechanism that applies the Activity-Based Benefit approach (ABBA) to solve the profitability for each customer from the market. ABBA provides financial and non-financial information for the operation, but also indicates what resources have expired in the operational process. The customer profit management model shows the level of profitability of each customer for the company. The empirical data were gathered from a case company operating in the leather industry in Taiwan. The research findings indicate that 30% of customers create little profit for the company as a result of asking for over 5% of sales discounts. Those customers ask for sales discount because of color differences of leather products. This paper provides a customer’s profitability evaluation mechanism to help enterprises to greatly improve operating effectiveness and promote operational activity efficiency and overall operation profitability.

Keywords: activity-based benefit approach, customer profit analysis, leather industry, profitability management mechanism

Procedia PDF Downloads 308

Authors: Omar Said Almushyt

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Quality management, in all countries all over the world, has become recently necessary to face challenges among companies. Software companies in KSA suffer from two problems, namely, low customer satisfaction, and low product quality. Implementation of quality management for a software company can solve these problems, by improving the quality of products and enhancing customer satisfaction. This will lead the company to be competitive. Introducing quality management system onto system analysis followed by system design and finally implementing that system can achieve these goals. Results of the present work showed that the proposed method can increase both the product quality by 10 % and the customer satisfaction by 20 %.

Keywords: quality, management, software, information engineering

Procedia PDF Downloads 440

Authors: Odunayo S. Faluse, Roger Telfer

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Customer relationship management is a business strategy that is centred on the idea that ‘Customer is the driving force of any business’ i.e. Customer is placed in a central position in any business. However, this belief coupled with the advancement in information technology in the past twenty years has experienced a change. In any form of business today it can be concluded that customers are the modern dictators to whom the industry always adjusts its business operations due to the increase in availability of information, intense market competition and ever growing negotiating ideas of customers in the process of buying and selling. The most vital role of any organization is to satisfy or meet customer’s needs and demands, which eventually determines customer’s long-term value to the industry. Therefore, this paper analyses and describes the application of customer relationship management operational strategies in some of the major industries in business. Both developed and up-coming companies nowadays value the quality of customer services and client’s loyalty, they also recognize the customers that are not very sensitive when it comes to changes in price and thereby realize that attracting new customers is more tasking and expensive than retaining the existing customers. However, research shows that several factors have recently amounts to the sudden rise in the execution of CRM strategies in the marketplace, such as a diverted attention of some organization towards integrating ideas in retaining existing customers rather than attracting new one, gathering data about customers through the use of internal database system and acquiring of external syndicate data, also exponential increase in technological intelligence. Apparently, with this development in business operations, CRM research in Academia remain nascent; hence this paper gives detailed critical analysis of the recent advancement in the use of CRM and key research opportunities for future development in using the implementation of CRM as a determinant factor for successful business optimization.

Keywords: agriculture, banking, business strategies, CRM, education, healthcare

Procedia PDF Downloads 224

Authors: E. J. Cilliers, Z. Goosen

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The impact that urban green spaces have on sustainability and quality of life is phenomenal. This is also true for the local South African environment. However, in reality green spaces in urban environments are decreasing due to growing populations, increasing urbanization and development pressure. This further impacts on the provision of child-friendly spaces, a concept that is already limited in local context. Child-friendly spaces are described as environments in which people (children) feel intimately connected to, influencing the physical, social, emotional, and ecological health of individuals and communities. The benefits of providing such spaces for the youth are well documented in literature. This research therefore aimed to investigate the concept of child-friendly spaces and its applicability to the South African planning context, in order to guide the planning of such spaces for future communities and use. Child-friendly spaces in the urban environment of the city of Durban, was used as local case study, along with two international case studies namely Mullerpier public playground in Rotterdam, the Netherlands, and Kadidjiny Park in Melville, Australia. The aim was to determine how these spaces were planned and developed and to identify tools that were used to accomplish the goal of providing successful child-friendly green spaces within urban areas. The need and significance of planning for such spaces was portrayed within the international case studies. It is confirmed that minimal provision is made for green space planning within the South African context, when there is reflected on the international examples. As a result international examples and disciples of providing child-friendly green spaces should direct planning guidelines within local context. The research concluded that child-friendly green spaces have a positive impact on the urban environment and assist in a child’s development and interaction with the natural environment. Regrettably, the planning of these child-friendly spaces is not given priority within current spatial plans, despite the proven benefits of such.

Keywords: built environment, child-friendly spaces, green spaces, public places, urban area

Procedia PDF Downloads 445

Authors: Aya Belal, Ahmed Maher Eltair, Maggie Ahmed Mashaly

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Geopolymer concrete is gaining recognition as a sustainable alternative to conventional Portland Cement concrete due to its environmentally friendly nature, which is a key goal for Smart City initiatives. It has demonstrated its potential as a reliable material for the design of structural elements. However, the production of Geopolymer concrete is hindered by batch-to-batch variations, which presents a significant challenge to the widespread adoption of Geopolymer concrete. To date, Machine learning has had a profound impact on various fields by enabling models to learn from large datasets and predict outputs accurately. This paper proposes an integration between the current drift to Artificial Intelligence and the composition of Geopolymer mixtures to predict their mechanical properties. This study employs Python software to develop machine learning model in specific Decision Trees. The research uses the percentage oxides and the chemical composition of the Alkali Solution along with the curing conditions as the input independent parameters, irrespective of the waste products used in the mixture yielding the compressive strength of the mix as the output parameter. The results showed 90 % agreement of the predicted values to the actual values having the ratio of the Sodium Silicate to the Sodium Hydroxide solution being the dominant parameter in the mixture.

Keywords: decision trees, geopolymer concrete, machine learning, smart cities, sustainability

Procedia PDF Downloads 89

Authors: Kwaku Damoah

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This paper presents a methodology and software application (App) designed to empower users in accessing, retrieving, and comparatively exploring data within the hierarchical network framework of the Government Finance Statistics (GFS) system. It explores the ease of navigating the GFS system and identifies the gaps filled by the new methodology and App. The GFS, embodies a complex Hierarchical Network Classification (HNC) structure, encapsulating institutional units, revenues, expenses, assets, liabilities, and economic activities. Navigating this structure demands specialized knowledge, experience, and skill, posing a significant challenge for effective analytics and fiscal policy decision-making. Many professionals encounter difficulties deciphering these classifications, hindering confident utilization of the system. This accessibility barrier obstructs a vast number of professionals, students, policymakers, and the public from leveraging the abundant data and information within the GFS. Leveraging R programming language, Data Science Analytics and Machine Learning, an efficient methodology enabling users to access, navigate, and conduct exploratory comparisons was developed. The machine learning Fiscal Analytics App (FLOWZZ) democratizes access to advanced analytics through its user-friendly interface, breaking down expertise barriers.

Keywords: data science, data wrangling, drilldown analytics, government finance statistics, hierarchical network classification, machine learning, web application.

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Authors: Noor Hasmini Abdghani, Nik Kamariah Nikmat, Nor Hayati Ahmad

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Rapid growth of the internet technology led to changes in the consumer lifestyles. This involved customer buying behaviour-based internet that create new kind of buying strategy. Hence, it has summoned many of world firms including Malaysia to generate new quality strategy in preparation to face new customer buying lifestyles. Particularly, this study focused on identifying online customer segment of automobile passenger car customers. Secondly, the objective is to understand online customer’s receptiveness towards internet technologies. This study distributed 700 questionnaires whereby 582 were returned representing 83% response rate. The data were analysed using factor and regression analyses. The result from the factor analysis precipitates four online passenger car segmentations in Malaysia, which are: Segment (1)- Automobile Online shopping Preferences, Segment (2)- Automobile Online Brand Comparison, Segment (3)- Automobile Online Information Seeking and Segment (4)- Automobile Offline Shopping Preferences. In understanding the online customer’s receptiveness towards internet, the regression result shows that there is significant relationship between each of four segments of online passenger car customer with attitude towards automobile online shopping. This implies that, for online customers to have receptiveness toward internet technologies, he or she must have preferences toward online shopping or at least prefer to browse any related information online even if the actual purchase is made at the traditional store. With this proposed segmentation strategy, the firms especially the automobile firms will be able to understand their online customer behavior. At least, the proposed segmentation strategy will help the firms to strategize quality management approach for their online customers’ buying decision making.

Keywords: Automobile, Market Segmentation, Online Shopping Attitude, Quality Management Strategy

Procedia PDF Downloads 541

Authors: Fatima S. Rodriguez, Victorino T. Taylan, Manolito C. Bulaong, Helen F. Gavino, Vitaliana U. Malamug

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In sundrying, the quality of the grains are greatly reduced when paddy grains were caught by the rain unsacked and unstored resulting to reduced profit. The objectives of this study were to design and fabricate a quick covering machine for grain drying pavement to test and evaluate the operating characteristics of the machine according to its deployment speed, recovery speed, deployment time, recovery time, power consumption, aesthetics of laminated sack, conducting partial budget, and cost curve analysis. The machine was able to cover the grains in a 12.8 m x 22.5 m grain drying pavement at an average time of 17.13 s. It consumed 0 .53 W-hr for the deployment and recovery of the cover. The machine entailed an investment cost of $1,344.40 and an annual cost charge of $647.32. Moreover, the savings per year using the quick covering machine was $101.83.

Keywords: quick, covering machine, grain, drying pavement

Procedia PDF Downloads 375

Authors: Jaishree Ranganathan, Poonam Rajurkar, Angelina A. Tzacheva, Zbigniew W. Ras

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In today’s world, business often depends on the customer feedback and reviews. Sentiment analysis helps identify and extract information about the sentiment or emotion of the of the topic or document. Attribute selection is a challenging problem, especially with large datasets in actionable pattern mining algorithms. Action Rule Mining is one of the methods to discover actionable patterns from data. Action Rules are rules that help describe specific actions to be made in the form of conditions that help achieve the desired outcome. The rules help to change from any undesirable or negative state to a more desirable or positive state. In this paper, we present a Lexicon based weighted scheme approach to identify emotions from customer feedback data in the area of manufacturing business. Also, we use Rough sets and explore the attribute selection method for large scale datasets. Then we apply Actionable pattern mining to extract possible emotion change recommendations. This kind of recommendations help business analyst to improve their customer service which leads to customer satisfaction and increase sales revenue.

Keywords: actionable pattern discovery, attribute selection, business data, data mining, emotion

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Authors: Vaishali Joshi, Pallav Joshi

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Environment concern has become an important topic and continues to acquire more popularity in the coming scenario. We all are exposed to messages daily, which encourage us to involve in green behavior. Factors such as Global Warming, Climate change are creating a big buzz amongst the people. Realizing this, many firms/companies are adopting the bright way of making profit along with creating a brand image, by going green. These firms/companies persuade consumers to use purchase eco-friendly products for the benefit of the environment and the society. In such scenario, it becomes very essential for such firms/companies to approach the customers in a better way. In other words, we can say that marketing approach plays a crucial role for such firm/companies. Hence in this research study, we have tried to create a marketing approach for the firms/companies for selling the eco-friendly apparels. We have studied the hypothetical apparel brand who has taken a green initiative of making their products eco-friendly. We have named this hypothetical brand as “Go-Green”. By taking this hypothetical brand we have studied about how this brand can achieve better marketing approach. In particular, we have studied the four types of print advertisements of this brand as follows :(i) print advertisement showing only eco-friendly apparel (ii) print advertisement showing eco-friendly apparel labeled with eco-label (iii) print advertisement showing eco-friendly apparel along with information about the benefit of the featured apparel and (iv) print advertisement showing eco-friendly apparel with both eco-label and information about the benefit of the featured apparel. The conclusion of this research suggest that respondents more positively evaluate the print advertisement of eco-friendly apparel labeled with eco-labels and information about the benefit of the featured apparel, compared by other three print advertisement. Moreover, in this research study, we have studied environment knowledge, as the moderating factor affecting the consumer green purchase behavior.

Keywords: eco-friendly apparel, print advertisement, eco-label, environment knowledge

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Authors: Joanna Maj

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Awe inspiring is the idea that emotions could be present in human-machine interactions, both on the human side as well as the machine side. Human factors present intriguing components and are examined in detail while discussing this controversial topic. Mood, attention, memory, performance, assessment, causes of emotion, and neurological responses are analyzed as components of the interaction. Problems in computer-based technology, revenge of the system on its users and design, and applications comprise a major part of all descriptions and examples throughout this paper. It also allows for critical thinking while challenging intriguing questions regarding future directions in research, dealing with emotion in human-machine interactions.

Keywords: biocomputing, biomedical engineering, emotions, human-machine interaction, interfaces

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Authors: Bibi Somayeh Aliakbari, Niknaz Kachooei, Fatemeh Amiri Najafabadi

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According to the results of contemporary urbanism, social living moved into buildings and the quality of urban space has been declining. But still, there are life in open public space and it is one of reason attendance and activities of people in open public spaces.The purpose of this research is finding reason creation friendly public space in urban spaces and also use these in new urban spaces.The research methodology consisted of a qualitative model based on observation and graphical analysis. In this paper case study is public space historical, moderns in urban scales and local scales in Iran.This paper shows that Existence of friendly public space in cities cause is attendance and activities of people in open public spaces that it is reason the revitalization of public open spaces in cities.

Keywords: public space, public open space, friendly public space, Iran

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Authors: Hicham Amine, Abdelouahab Mesnaoui

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Electronic data interchange EDI is the computer-to-computer exchange of structured business information. This information typically takes the form of standardized electronic business documents, such as invoices, purchase orders, bills of lading, and so on. The purpose of this study is to identify the impact EDI might have on supply chain and typically on customer satisfaction keeping in mind the constraints the organization might face. This study included 139 subject matter experts (SMEs) who participated by responding to a survey that was distributed. 85% responded that they are extremely for the implementation while 10% were neutral and 5% were against the implementation. From the quality assurance department, we have got 75% from the clients agreed to move on with the change whereas 10% stayed neutral and finally 15% were against the change. From the legal department where 80% of the answers were for the implementation and 10% of the participants stayed neutral whereas the last 10% were against it. The survey consisted of 40% male and 60% female (sex-ratio (F/M=1,5), who had chosen to participate. Our survey also contained 3 categories in terms of technical background where 80% are from technical background and 15% were from nontechnical background and 5% had some average technical background. This study examines the impact of EDI on customer satisfaction which is the primary hypothesis and justifies the importance of the implementation which enhances the customer satisfaction.

Keywords: electronic data interchange, supply chain, subject matter experts, customer satisfaction

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Authors: Ali R. Alshawawreh, Francisco Liébana-Cabanillas, Francisco J. Blanco-Encomienda

Abstract:

Collaboration between marketing and business intelligence (BI) is crucial in today's ever-evolving business landscape. These two domains play pivotal roles in molding customers' experiential knowledge. Marketing insights offer valuable information regarding customer needs, preferences, and behaviors. Conversely, BI facilitates data-driven decision-making, leading to heightened operational efficiency, product quality, and customer satisfaction. Customer experiential knowledge (CEK) encompasses customers' implicit comprehension of consumption experiences influenced by diverse factors, including social and cultural influences. This study primarily focuses on telecommunications companies in Jordan, scrutinizing how experiential customer knowledge mediates the relationship between marketing intelligence and business intelligence. Drawing on theoretical frameworks such as the resource-based view (RBV) and service-dominant logic (SDL), the research aims to comprehend how organizations utilize their resources, particularly knowledge, to foster Evolution. Employing a quantitative research approach, the study collected and analyzed primary data to explore hypotheses. Structural equation modeling (SEM) facilitated by Smart PLS software evaluated the relationships between the constructs, followed by mediation analysis to assess the indirect associations in the model. The study findings offer insights into the intricate dynamics of organizational Creation, uncovering the interconnected relationships between business intelligence, customer experiential knowledge-based innovation (CEK-DI), marketing intelligence (MI), and product and service innovation (PSI), underscoring the pivotal role of advanced intelligence capabilities in developing innovative practices rooted in a profound understanding of customer experiences. Furthermore, the positive impact of BI on PSI reaffirms the significance of data-driven decision-making in shaping the innovation landscape. The significant impact of CEK-DI on PSI highlights the critical role of customer experiences in driving an organization. Companies that actively integrate customer insights into their opportunity creation processes are more likely to create offerings that match customer expectations, which drives higher levels of product and service sophistication. Additionally, the positive and significant impact of MI on CEK-DI underscores the critical role of market insights in shaping evolutionary strategies. While the relationship between MI and PSI is positive, the slightly weaker significance level indicates a subtle association, suggesting that while MI contributes to the development of ideas, In conclusion, the study emphasizes the fundamental role of intelligence capabilities, especially artificial intelligence, emphasizing the need for organizations to leverage market and customer intelligence to achieve effective and competitive innovation practices. Collaborative efforts between marketing and business intelligence serve as pivotal drivers of development, influencing customer experiential knowledge and shaping organizational strategies and practices. Future research could adopt longitudinal designs and gather data from various sectors to offer broader insights. Additionally, the study focuses on the effects of marketing intelligence, business intelligence, customer experiential knowledge, and innovation, but other unexamined variables may also influence innovation processes. Future studies could investigate additional factors, mediators, or moderators, including the role of emerging technologies like AI and machine learning in driving innovation.

Keywords: marketing intelligence, business intelligence, product, customer experiential knowledge-driven innovation

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Authors: Bokkasam Sasidhar, Ibrahim Aljasser

Abstract:

The problem of finding optimal schedules for each equipment in a production process is considered, which consists of a single stage of manufacturing and which can handle different types of products, where changeover for handling one type of product to the other type incurs certain costs. The machine capacity is determined by the upper limit for the quantity that can be processed for each of the products in a set up. The changeover costs increase with the number of set ups and hence to minimize the costs associated with the product changeover, the planning should be such that similar types of products should be processed successively so that the total number of changeovers and in turn the associated set up costs are minimized. The problem of cost minimization is equivalent to the problem of minimizing the number of set ups or equivalently maximizing the capacity utilization in between every set up or maximizing the total capacity utilization. Further, the production is usually planned against customers’ orders, and generally different customers’ orders are assigned one of the two priorities – “normal” or “priority” order. The problem of production planning in such a situation can be formulated into a Multiple Arc Network (MAN) model and can be solved sequentially using the algorithm for maximizing flow along a MAN and the algorithm for maximizing flow along a MAN with priority arcs. The model aims to provide optimal production schedule with an objective of maximizing capacity utilization, so that the customer-wise delivery schedules are fulfilled, keeping in view the customer priorities. Algorithms have been presented for solving the MAN formulation of the production planning with customer priorities. The application of the model is demonstrated through numerical examples.

Keywords: scheduling, maximal flow problem, multiple arc network model, optimization

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Authors: Majid Mohammadi, Mohammad Yosef Zadeh, Vahid Naderi Darshori

Abstract:

The main purpose of this research is concreting of satisfaction literature for obtain index with online content in carmaker industry. The study measures customer satisfaction of online and collect from similar studies with reference to a model of online satisfaction, they are attempting to complete. Statistical communities of research are online customers' carmaker Iran Khodro has been buying the company's products in the last six months. One of the innovative measures in this study is that, customer reviews are obtained through an Internet site. Reliability of the data collected in this study, the Cronbach's alpha coefficient was approved. The coefficient of 0.828 was calculated for the questionnaire. To test the hypothesis, the Pearson correlation coefficient was used. To ensure the correctness of initial theoretical model, we used regression analyzes and structural equation weight and finally, the results obtained with little change to the basic model of research, are improved and completed. At last obtain the perceived value has most direct effect on online car customers satisfaction.

Keywords: customer satisfaction, online satisfaction, online customer, car

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Authors: Teerapong Lorchitamnuay, Thirarut Worapishet

Abstract:

In the present digital age, today's customers explore various channels before finalizing a purchase, with abundant options and information at their disposal. Despite this, there is a strong digital interconnectedness. With just a few mouse clicks, customers can gather comprehensive information about a product, free from the influence of a salesperson. Salespeople must embrace cutting-edge technology to truly redefine the essence of selling if they are to thrive in this digital era. The significance of customer-salesperson communication in companies is becoming increasingly evident. It prompts the inquiry of how companies can modify or reshape their sales teams' approaches to effectively respond to evolving customer preferences and effectively manage external shifts, all in pursuit of sustaining and expanding their enterprises. Research highlights that digital and intercultural skills are the latest competencies sought by customers from salespeople in today's fast-paced world prior to making purchases of products and services. This study seeks to examine the pivotal influences of these salesperson skills in achieving customer satisfaction. The research design encompasses the analysis of descriptive statistics and quantitative data through a regression model. Data were gathered from an online convenience survey involving 260 respondents who are customers of an air express service provider in Thailand and who engage with salespeople in a traditional manner. The findings underscore that intercultural skills have a substantial impact on customer satisfaction in the digital era, particularly concerning adaptability, foreign language proficiency, active listening, and empathy skills. Organizations should focus on nurturing beneficial habits among their salespeople; since it signifies this effort, it should extend beyond just the frontline but should extend to encompass backline units and high-level management, ensuring that everyone possesses the same customer-oriented skills. The conclusions drawn from this research provide valuable insights, affirming that digital and intercultural skills can empower organizations to optimize their workforce's competencies, thereby achieving customer satisfaction in the digital age.

Keywords: customer behavior, customer satisfaction, digital era, digital skill, intercultural skill

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Authors: Stephen Coandadiputra, Chrestella Carissa

Abstract:

Technology is created to simplify human’s life. One of current technology which being called as the second wave internet generation is the internet of things. Internet of things lets thousands of devices connected each other. In today's marketing world, IOT has brought customer into the next level which helping the customer to shorten every transaction they are conducting from traditional approach to sophisticated approach. However, the implementation of technology has always obstacles. The objective of this paper is to explore the determinants of customer to accepts such technology like the internet of things within their transaction. According to TAM (Technology Acceptance Model), researcher constructs the acceptance of internet of things based on perceived usefulness, perceived ease of use and trust and social factor and the two customer characteristics: perceived enjoyment and perceived behavioral control. This research uses exploratory research design which being facilitated by spreading questionnaire to 145 T-cash users in Jakarta and in its suburban region. At least, 190 samples were observed and questioned accordingly. All the collected data will be analyzed using Lisrel.

Keywords: behavioral intention to use, internet of things, near field communication, technology acceptance model

Procedia PDF Downloads 336