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The credit card business has been one of the key businesses for banks in Taiwan. The purpose of this paper is to use competitive dynamics and structured context analysis (SCA) to explore the competition relationships among market, resources, and strategies concerning the credit card issued banks in Taiwan.

The market commonality and resource similarity analysis of competitive dynamics in the first stage obtained the competitive mapping of four major credit card issue banks, as well as the differences of competition strategy. In the second stage, 1,968 pieces of data on credit card news from 2013 to 2014 were collected. SCA was used to analyze the competitive action, competitive response, number of responses, response lag, and response order.

The competitor mapping and four hypothesis obtained from competitive dynamics correspond to the credit card competition strategy, as obtained from SCA.

This research combined competitive dynamics and SCA to analyze the credit cards market in Taiwan. The research model could be used in the other financial market.

The purpose of this study is to investigate the effects of the Perceptions of coronavirus 2019 (COVID-19) questionnaire and the Information System Success Questionnaire on students when using the school's COVID-19 epidemic prevention website. The study is aimed toward analyzing both questionnaires as well as evaluating an epidemic prevention website.

The school's COVID-19 prevention website and two questionnaires (Perceptions of COVID-19 and the Information System Success Questionnaire) are examined in order to investigate 73 students' COVID-19 perceptions. An open-ended question was used as the qualitative data to support quantitative data and evaluate a university's COVID-19 epidemic prevention website from a southern university in Taiwan.

The findings indicated that most students evaluated the school's COVID-19 website positively and were satisfied. In the open-ended questions, the majority of students rated the quality of the system positively and the need to fix some defects. Students have different COVID-19 perceptions and social distance compliance based on their current situations.

This study provides researchers and website developers a broader understanding of the construction of the school's COVID-19 prevention website and a better understanding of student's COVID-19 perceptions.

To the best of the authors' knowledge, this is the first study examining a school's epidemic prevention website, which is measured by the Information Success Questionnaire and the Perceptions of COVID-19 Questionnaire for college students.

This paper proposes a four‐state framework for measuring corporate social responsibility reporting and argues that TQM or excellent organisations should be in an advanced state of social responsiveness, and that this should be reflected in their reporting of such activities. A study of six Irish financial institutions is used to demonstrate the extent of social reporting in company annual reports and Web sites with a view to positioning them on the framework. Social reporting in the annual reports is compared with four European “best practice” financial institutions as well as the organisations' Web sites. Analysis shows that Irish banks are well behind the leading European banks with regard to the quality and quantity of social disclosure in their annual reports. It further shows that they disclose a greater volume of social information on their Web sites than in their annual reports. Reasons for such poor performance are attributed to the voluntary nature of social disclosure in Ireland.

Decision makers in the service industry must effectively cope with queuing problems, service capacity optimization, service efficiency and service quality problems. This study proposes a computer simulation‐enabled MCDM framework that integrates computer simulation analysis, Taguchi method, expert opinion and multiple criteria decision making (MCDM) to assist decision makers in coping with decision problems. In this framework, Taguchi method is adopted to reduce the time required for the simulation experiment. Computer simulation analysis is adopted to obtain useful information for rapid decision‐making without interrupting actual production. MCDM is used to select the optimal alternative. The illustrative result is extremely promising.

This research has three main objectives. First, it examines influence of gamification on the behavioral intention to use an e-commerce platform. Second, it analyzes the role of the flow state given its importance in terms of behavior in online environments. Finally, the study aims to detect and analyze differences between Millennials and Generation X.

The theoretical basis for this study stems from technology acceptance model. The extended model incorporates gamification and the optimal state of intrinsic motivation, flow state, as additional constructs. An online consumer panel was used to collect data from 253 Spanish Amazon users. A structural equation modeling, partial least squares, is proposed and multi-group moderation was studied.

Gamification in Millennials has positive and significant indirect effects on behavioral intention through the flow state. In the case of the Generation X, it has been detected that flow interferes in its perception of ease of use. The behavioral intention of using the Web page is directly correlated with the purchase intention. Companies should offer a fun interface to Millennials and an environment easier to use to the Generation X, for gamification to be successful.

This study expands the research scope in gamification by focusing on e-commerce sector, a field where scientific research is still scarcely developed. It emphasizes the importance of flow as mediator. Age differences confirm the need for segmentation when applying gamification and marketing strategies in e-commerce.

It is known that a conflict exists between consumers’ materialistic and green values. Previous research has focused on values conflict consequences. Antecedents of consumers’ materialistic and green values conflict remain understudied. This study aims to explore the antecedents of consumers’ materialistic and green values conflict.

An exploratory type research design was applied. Overall 22 interviews were conducted with consumers that had materialistic and green values conflict. The transcripts of the interviews were analyzed using content analysis with Maxqda software.

The findings demonstrate consumers' negative attitudes towards consumption, understood as consumerism. Results indicate that value conflict is related to unpleasant emotions such as guilt, anxiety, helplessness and remorse. Guilt is the most prominent emotion associated with the conflict of values. The study identifies dissonant information, environmental knowledge, social norms, impulsive buying and mindfulness as antecedents of materialistic and green values conflict.

The novelty of the study is the antecedents of the materialistic and green values conflict. This study makes a valuable contribution to the academic discourse on sustainable consumption, consumer materialism and green values by providing a deeper understanding of the values conflict experienced by consumers who hold materialistic and green values. The main significance of this study is that it provides valuable insights from qualitative research into the antecedents of the conflict between consumers' materialistic and green values.

This review provides an overview of recent advances in electrochemical sensors for analyte detection in saliva, highlighting their potential applications in diagnostics and health care. The purpose of this paper is to summarize the current state of the field, identify challenges and limitations and discuss future prospects for the development of saliva-based electrochemical sensors.

The paper reviews relevant literature and research articles to examine the latest developments in electrochemical sensing technologies for saliva analysis. It explores the use of various electrode materials, including carbon nanomaterial, metal nanoparticles and conducting polymers, as well as the integration of microfluidics, lab-on-a-chip (LOC) devices and wearable/implantable technologies. The design and fabrication methodologies used in these sensors are discussed, along with sample preparation techniques and biorecognition elements for enhancing sensor performance.

Electrochemical sensors for salivary analyte detection have demonstrated excellent potential for noninvasive, rapid and cost-effective diagnostics. Recent advancements have resulted in improved sensor selectivity, stability, sensitivity and compatibility with complex saliva samples. Integration with microfluidics and LOC technologies has shown promise in enhancing sensor efficiency and accuracy. In addition, wearable and implantable sensors enable continuous, real-time monitoring of salivary analytes, opening new avenues for personalized health care and disease management.

This review presents an up-to-date overview of electrochemical sensors for analyte detection in saliva, offering insights into their design, fabrication and performance. It highlights the originality and value of integrating electrochemical sensing with microfluidics, wearable/implantable technologies and point-of-care testing platforms. The review also identifies challenges and limitations, such as interference from other saliva components and the need for improved stability and reproducibility. Future prospects include the development of novel microfluidic devices, advanced materials and user-friendly diagnostic devices to unlock the full potential of saliva-based electrochemical sensing in clinical practice.

Watermarking technique is one of the significant methods in which carrier signal hides digital information in the form of watermark to prevent the authenticity of the stakeholders by manipulating different coefficients as watermark in time and frequency domain to sustain trade-off in performance parameters. One challenging component among others is to maintain the robustness, to limit perceptibility with embedding information. Transform domain is more popular to achieve the required results in color image watermarking. Variants of complex Hadamard transform (CHT) have been applied for gray image watermarking, and it has been proved that it has better performance than other orthogonal transforms. This paper is aimed at analyzing the performance of spatio-chromatic complex Hadamard transform (Sp-CHT) that is proposed as an application of color image watermarking in sequency domain (SD).

In this paper, color image watermarking technique is designed and implemented in SD using spatio-chromatic – conjugate symmetric sequency – ordered CHT. The color of a pixel is represented as complex number a*+jb*, where a* and b* are chromatic components of International Commission on Illumination (CIE) La*b* color space. The embedded watermark is almost transparent to human eye although robust against common signal processing attacks.

Based on the results, bit error rate (BER) and peak signal to noise ratio are measured and discussed in comparison of CIE La*b* and hue, saturation and value color model with spatio-chromatic discrete Fourier transform (Sp-DFT), and results are also analyzed with other discrete orthogonal transforms. It is observed from BER that Sp-CHT has 8%–12% better performance than Sp-DFT. Structural similarity index has been measured at different watermark strength and it is observed that presented transform performs better than other transforms.

This work presents the details and comparative analysis of two orthogonal transforms as color image watermarking application using MATLAB software. A finding from this study demonstrates that the Complex Hadamard transform is the competent candidate that can be replaced with DFT in many signal processing applications.

The surface energy per unit area of material is known to be proportional to the thermal energy at the melting point of the material. The purpose of this paper is to employ the values of the melting points of metals to develop a model that estimates the average surface energies of metals. Average surface energy estimator (ASEE) was developed with the aid of computational intelligence technique on the platform of support vector regression (SVR) using the values of the melting point of the materials as the descriptor.

The development of ASEE which involves 12 data set was conducted by training and testing SVR model using test-set-cross-validation technique. The developed model (ASEE) was used to estimate average surface energies of 3d, 4d, 5d and other selected metals in the periodic table. The average surface energies obtained from ASEE are in good agreement with the experimental values and with the values from other theoretical models.

The accuracy of this developed model coupled with its adoption of descriptor that can be easily obtained makes it a viable alternative in circumventing the difficulty experienced in experimental determination of average surface energies of materials.

Modeling of ASEE has never been reported in the literature. Meanwhile, the use of ASEE will help circumvent the difficulties involved in the experimental determination of average surface energies of materials.