Design Principles for Diffusion of Reports and Innovative Use of Business Intelligence Platforms

In order to innovate and respond quickly to new requirements, employees frequently supplement their information systems. This particularly applies to the context of business intelligence (BI) because many users supplement their BI platforms with individually tinkered spreadsheets. Unfortunately, these supplements bear numerous threats such as limited report reuse across all potential users. To address this gap, we establish a design science project. First, we qualitatively explore impediments to diffusion of reports and impediments to innovative use. Second, upon our findings and extant literature, we derive meta-requirements for BI platforms that foster diffusion of reports and innovative use. Third, we develop and discuss principles for how to design a BI platform that would meet the identified meta-requirements. The resulting design principles emphasize (1) permanent user sandboxes to improve innovative use and (2) hybrid recommendation agents based on user interaction, collaborative-filtering, and users\u27 social influence to improve diffusion of reports

Artificial Intelligence Techniques in E-Commerce: The Possibility of Exploiting them in Saudi Arabia

E-Commerce has transformed business as we know over the past few decades. The rapid increasing use of the Internet and the strong purchasing power in Saudi Arabia have had a strong impact on the evolution of E-Commerce in the country. Saudi Arabia is yet another country that will release artificial intelligence power to fuel its growth in the economic world.  Recently, artificial intelligence (AI) applications that can facilitate e-commerce processes have been widely used. The impact of using artificial intelligence (AI) concepts and techniques on the efficiency of e-commerce, particularly has been overlooked by many prior studies. In this paper, a literature review was conducted to explore and investigate possible applications of AI in E-Commerce that can help Saudi Arabian businesses

Experiences with RFID-Based Interactive Learning in Museums

Tourism plays an important role in the economies of many countries. Tourism can secure employment, foreign exchange earnings, investment and regional development. To attract more tourists and local visitors, many stakeholders such as natural parks, museums, art galleries, hotels and restaurants provide personalised services to meet individual needs. With the increasing number of tourists comes an increased demand for guides at education-oriented leisure centers. Each provided needs unique way to present their services. In this study, these educational leisure centres are coarsely divided into art and science. This paper introduces the architecture of the proposed guide system including a PDA-based recommendation guide for art museums and an Radiofrequency identification-based interactive learning system using collaborative filtering technology for science and engineering education. Evaluations of the two systems reveal that the system inspires and nurtures visitors’ interest in science and arts

A Framework for Online Detection and Reaction to Disturbances on the Shop Floor Using Process Mining and Machine Learning

The shop floor is a dynamic environment, where deviations to the production plan frequently occur. While there are many tools to support production planning, production control is left unsupported in handling disruptions. The production controller evaluates the deviations and selects the most suitable countermeasures based on his experience. The transparency should be increased in order to improve the decision quality of the production controller by providing meaningful information during his decision process. In this paper, we propose a framework in which an interactive production control system supports the controller in the identification of and reaction to disturbances on the shop floor. At the same time, the system is being improved and updated by the domain knowledge of the controller. The reference architecture consists of three main parts. The first part is the process mining platform, the second part is the machine learning subsystem that consists of a part for the classification of the disturbances and one part for recommending countermeasures to identified disturbances. The third part is the interactive user interface. Integrating the user’s feedback will enable an adaptation to the constantly changing constraints of production control. As an outlook for a technical realization, the design of the user interface and the way of interaction is presented. For the evaluation of our framework, we will use simulated event data of a sample production line. The implementation and test should result in higher production performance by reducing the downtime of the production and increase in its productivity

An architecture for user preference-based IoT service selection in cloud computing using mobile devices for smart campus

The Internet of things refers to the set of objects that have identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social environments and user context. Interconnected devices communicating to each other or to other machines on the network have increased the number of services. The concepts of discovery, brokerage, selection and reliability are important in dynamic environments. These concepts have emerged as an important field distinguished from conventional distributed computing by its focus on large-scale resource sharing, delivery and innovative applications. The usage of Internet of Things technology across different service provisioning environments has increased the challenges associated with service selection and discovery. Although a set of terms can be used to express requirements for the desired service, a more detailed and specific user interface would make it easy for the users to express their requirements using high-level constructs. In order to address the challenge of service selection and discovery, we developed an architecture that enables a representation of user preferences and manipulates relevant descriptions of available services. To ensure that the key components of the architecture work, algorithms (content-based and collaborative filtering) derived from the architecture were proposed. The architecture was tested by selecting services using content-based as well as collaborative algorithms. The performances of the algorithms were evaluated using response time. Their effectiveness was evaluated using recall and precision. The results showed that the content-based recommender system is more effective than the collaborative filtering recommender system. Furthermore, the results showed that the content-based technique is more time-efficient than the collaborative filtering technique

Dynamic Characteristic of Consumer Attention in Online Reviews —Empirical Research Based on Mobile Store Reviews

Nowadays consumer online reviews are becoming more and more important for enterprise decision-making. While the existing research seldom discussed review data from a dynamic perspective, especially ignored consumers\u27 attention change during the product life cycle. To study whether there are dynamic changes and the characteristics of changes in the attention degree of consumers in each phase of the product life cycle, this paper coded a specific node program to collect the online reviews data of the four mobile phones in the entire product life cycle and used python\u27s Chinese automatic word segmentation tool library to segment each word and count word frequency, and then a stepwise regression method was used to analyze the dynamic changes of consumer attention. The paper finds that consumers’ attention on logistics and products presented in online reviews show a downward trend, and the attention on brands shows an upward trend; There is no obvious change in the attention degree on services, prices, and promotion; On the different dimensions of products, there is a significant difference in the attention degree. The research results broad the research ideas of online reviews, provide decision-making basis for enterprises to grasp the characteristics of consumers at different stages and to formulate production and marketing strategies

Big Data Analytics in the Entertainment Industry: Audience Behavior Analysis, Content Recommendation, and Revenue Maximization

This research contributes to the understanding of the significant role of big data analytics in transforming the entertainment industry. In this study, we investigate the impact of big data analytics on the entertainment industry, focusing on three key aspects: audience behavior analysis, content recommendation, and revenue maximization. To understand audience behavior, entertainment companies leverage big data analytics to collect and analyze vast amounts of data from various sources, including social media platforms, streaming services, ticket sales, and website traffic. By analyzing viewer preferences, engagement metrics, and geographic information, companies gain valuable insights into audience behavior. These insights help in creating content that resonates with the target audience, optimizing future content creation, and tailoring marketing strategies based on geographical preferences. Furthermore, big data analytics plays a vital role in powering content recommendation systems. Through collaborative filtering and content-based filtering techniques, entertainment platforms personalize content recommendations based on user behavior, preferences, and historical data. This enhances user satisfaction and increases the likelihood of discovering relevant and appealing content. Hybrid approaches that combine collaborative and content-based filtering techniques are also explored to achieve more accurate and diverse recommendations. Moreover, big data analytics enables entertainment companies to optimize revenue generation strategies. By analyzing historical data, market trends, and consumer behavior, companies can implement dynamic pricing strategies, adjusting ticket prices, subscription fees, or content pricing based on demand and viewer preferences. Additionally, targeted advertising based on user data enhances advertising revenue by delivering personalized advertisements. Furthermore, analyzing market data and consumer behavior patterns helps optimize licensing agreements and content distribution strategies, maximizing revenue opportunities

The influencing factors of user loyalty on e-commerce shopping guide platform -- Case “Shenmezhidemai”

In recent years, the development of the online retail market has become more and more diversified. There are a large number of advertisements and products gathered on major e-commerce platforms. Consumers cannot efficiently select the products they want in the huge product pool, and it is also difficult for merchants to select high-quality products. The products are precisely oriented to consumers, so the e-commerce shopping guide industry has begun to spread, develop and become popular. To help consumers select high-quality goods more quickly, the e-commerce shopping guide platforms collect and integrate information and discounts for users, and provide users with decision-making suggestions. However, there is a phenomenon that users who are dissatisfied after purchasing a product because of product price reduction, quality problems or logistics problems become angry with the e-commerce shopping guide platform. As a result, users even quit and uninstall the e-commerce shopping guide platform completely. In fact, the result should be the responsibility of the merchant who sells the product. This thesis takes the e-commerce shopping guide platform “Shenmezhidemai” as the re-search object, and uses grounded theory, case study and in-depth interview to carry out this research. First of all, this thesis sorts out the relevant research on e-commerce shopping guide platform and user loyalty, and conducts an overview of the environment, development history and status quo, classification, characteristics and profit model of e-commerce shop-ping guide platform. Secondly, based on grounded theory and in-depth interview method, 20 people participated in the interview, and the interview records of about 20,000 words were obtained. Through open coding, axial coding, selective coding and other processes, the key influencing factors of e-commerce shopping guide platform user loyalty are analyzed and the theoretical model is constructed. Through the eight categories of user-related factors, information utility, system utility, platform reputation, recommending function, interactive function, price comparison function and cross-border shopping function, the model of influencing factors of e-commerce shopping guide platform user loyalty is carefully analyzed. Finally, aiming at the above eight categories, corresponding suggestions are put forward for e-commerce shopping guide platforms to cultivate and increase user loyalty. The thesis hopes to provide some implications and recommendations for the development of e-commerce shopping guide platforms