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Social manufacturing has emerged. It aims to integrate the manufacturing resources of micro- and small-scale manufacturing enterprises (MSMEs) and help MSMEs cope with the dynamic, service-oriented and personalized market demands. In social manufacturing, MSMEs cooperate with each other through manufacturing resource sharing. However, because MSMEs are distributed and decentralized, the efficiency of establishing reliable cooperation between MSMEs is relatively low. Therefore, this paper presents a blockchain-driven cyber-credit evaluation system (BCCES) to implement distributed cyber-credit evaluation. BCCES can provide reliable cyber-credit for distributed MSMEs without the trusted third party. This can improve the efficiency of establishing reliable cooperation among unauthentic MSMEs.

The paper proposes a BCCES to evaluate MSMEs' cyber-credit in decentralized environment. In BCCES, a cyber-credit evaluation model is proposed by improving set pair analysis (SPA) method, and cyber-credit smart contract and distributed consensus mechanism are designed according to the runtime logic of distributed cyber-credit evaluation.

The results confirmed that BCCES is feasible and effective to implement cyber-credit evaluation without the trusted third party. With the advantages of blockchain, BCCES can automatically realize cyber-credit evaluation through smart contract and distributed consensus. At the same time, BCCES can evaluate the real-time cyber-credit of MSMEs based on their latest service evaluation. In addition, we can design corresponding smart contracts according to actual requirements, which makes blockchain applicable to different distributed scenarios.

The paper combines blockchain and SPA to implement cyber-credit evaluation in social manufacturing and provides a new feasible idea for cyber-credit evaluation without the trusted third party. This can also provide MSMEs a reference of applying blockchain to other distributed scenarios through combining smart contract and different algorithms.

The purpose of this paper is to apply a new idea and scientific method to evaluate the enterprise's independent innovation capability accurately.

The matter‐element, extension set and independent function in extension theory are introduced briefly. Based on extension theory, the matter‐element model on comprehensive evaluation of the enterprise's independent innovation capability is established. Then, an instance which makes structural analysis on the enterprise's independent innovation capability with extension evaluation method is given.

The matter‐element model on comprehensive evaluation of the enterprise's independent innovation capability is established. Extension evaluation method can combine qualitative analysis and quantitative analysis to reflect the changing process of the objective appropriately. By means of the matter‐element model, the evaluation result shows which the grade does the enterprise's independent innovation capability belong to.

Extension evaluation method resorting to extension theory, cannot only reveal the evaluation grade and competitive advantage of an enterprise within the industry or within the scope of the evaluation, but also show comprehensive index level of the enterprise's independent innovation capability.

It is significant to monitor the enterprise's development and changes effectively, find the key factors influencing the enterprise's independent innovation capability and take effective measures in time.

New idea and scientific method are introduced into the enterprise's independent innovation activity, and it can integrate both qualitative and quantitative analysis to solve contradictory problems.

Reports on a job evaluation service provided for a national firm of wholesalers. Discusses how consultants were retained to establish an objective grading structure; devise an appropriate salary scale; introduce a means of rewarding merit; calculate the costs of the change‐over; and finally recommend a programme of implementation.

This paper reports results of analyses made at an all-female Gulf Arab university measuring the nature and extent of biases in students' evaluation of faculty. Comparisons are made with research reporting the nature of similar relationships in North America. Two issues are investigated: 1) What variables (if any) bias faculty evaluation results at an all-female Arab university? 2) Are biasing variables different in nature or magnitude to those reported at North America universities? Using the population of 13,300 faculty evaluation records collected over two school years at Zayed University, correlations of faculty evaluation results to nine potentially biasing factors are made. Results show biases to faculty evaluation results do exist. However, biases are small, and strikingly similar in nature to those reported at North American universities.

Because of the potential cost savings that online teaching evaluations can deliver, many universities are converting their student evaluations of teaching to this mode of administration. In this study, we examine the effects of transitioning administration of student evaluations from paper-and-pencil to online. We use data from accounting and other departments in the College of Business Administration of a large private, research university that converted from paper to online administration of teaching evaluations. We examine the average teaching effectiveness rating and response rate for instructors who taught the same course before and after the conversion. In addition, we survey a sample of accounting students to investigate their responses to online teaching evaluations. We find a significant increase in the average effectiveness rating and a significant decrease in response rate. Furthermore, we find that those instructors with lower ratings under the paper administration experience the greatest increase in ratings when the evaluations are converted to online administration. In a follow-up survey, we find that students who are highly motivated and have higher grade point averages are more likely to complete and provide higher evaluations with the online administration. We discuss the implications of our results to accounting and business education literature.

Social psychological perspectives on educational stratification offer explanations that bridge the macro and micro social worlds. However, while ethnoracial disparities in academic achievement are evident during the earliest grade levels, most social psychological research in this area has examined high school or college student samples and has used a black–white binary to operationalize race.

We use longitudinal structural equation models to examine links between academic self-efficacy beliefs and school performance among a national sample of diverse third- through eighth-grade students in the United States.

Contrary to hypotheses derived from the student identity literature, we find no evidence that elementary and middle school students from different ethnoracial backgrounds vary in the degree to which they selectively discount evaluative feedback in their academic self-efficacy construction, nor in the extent to which they demonstrate disrupted links between academic self-efficacy and subsequent academic performance.

The study examines the extent to which race-linked social psychological processes may be driving academic achievement inequalities during the primary schooling years.

Student evaluation of teaching (SET) questionnaires are used in many countries, although much current research questions the validity of these surveys. US research indicates that more than 90 percent of academic accounting departments use this performance measurement. This paper aims to focus on the validity of SET data.

A mail survey was sent to a random sample of 1,000 accounting professors employed at four‐year universities and colleges in the USA. A total of 447 responses were returned for a response rate of 44.7 percent. Statistical results of the survey for data are reported.

Instructors engage in impression management when SET data are used for control purposes. Dysfunctional behavior of accounting instructors includes easy grading, inflating grades, course work deflation, and other defensive strategies which result in negative social implications. A significant 53 percent of the accounting instructors knew of other professors who have reduced grading standards and course content in order to improve SET scores.

Universities worldwide risk legal action when they defame faculty members by releasing unreliable and invalid SET results.

The paper illustrates some of the present problems with SET questionnaires.

The applicability of total quality management (TQM) to higher education instruction is controversial. The purpose of this paper is to help clarify the application of TQM to higher education instruction by identifying and analyzing the dual roles played by both students and instructors. The authors also offer an improvement to the instructor evaluation process designed to eliminate some of the negative effects of the duality of roles.

This is a conceptual paper discussing some of the shortcomings in previous discussions of the applicability of TQM to higher education instruction. In addition, it introduces the concept of the dual roles of students (customers/grade‐seekers) and faculty (suppliers/retention‐seekers), and analyzes their relationship to, and effect on, the evaluation of the educational process through the interaction of these dual roles.

This paper helps understand more fully the student/instructor roles and will contribute to a better understanding of the applicability of TQM to higher education. It offers a methodology to enhance the faculty evaluation process as a measure of instructional quality and offers actions that will strengthen the knowledge‐seeking motivation of students.

This paper offers a more thorough analysis of the roles of students and faculty than previous discussions of the TQM in higher education. The value of this paper is the identification of two pairs of contradictory roles played by both participants in the instruction process which complicates an understanding of the concept of quality and applicability of TQM to higher education.

The purpose of this paper is to propose a novel method using support vector machine (SVM) classifiers for objective seam pucker evaluation. Features are extracted using wavelet analysis and gray-level co-occurrence matrix (GLCM), and the samples are evaluated using SVM classifiers. The study aims to solve the problem of inappropriate parameters and large required samples in objective seam pucker evaluation.

Initially, seam pucker image was captured, and Edge detection and Hough transform were utilized to normalize the seam position and orientation. After cropping the image, the intensity was adjusted to the same identical level through histogram specification. Then, the standard deviations of the horizontal image and diagonal image, reconstructed using wavelet decomposition and reconstruction, were calculated based on parameter optimization. Meanwhile, GLCM was extracted from the restructured horizontal detail image, then the contrast and correlation of GLCM were calculated. Finally, these four features were imported to SVM classifiers based on genetic algorithm for evaluation.

The four extracted features reflected linear relationships among five grades. The experimental results showed that the classification accuracy was 96 percent, which catches up to the performance of human vision, and resolves ambiguity and subjective of the manual evaluation.

There are large required samples in current research. This paper provides a novel method using finite samples, and the parameters of the methods were discussed for parameter optimization. The evaluation results can provide references for analyzing the reason of wrinkles during garment manufacturing.

This study aims to enhance the assessment of green governance in energy projects along the Belt and Road, reduce the influence of fuzzy judgment, and construct a grey network analysis model from the perspective of Environmental, Social, and Governance (ESG).

The ESG concept is used to establish an evaluation indicator system. The Analytic Network Process (ANP) and the Grey System Theory are applied sequentially to determine the green governance grade of energy projects, exemplified by an evaluation of five projects.

The Karot hydropower project has the best green governance status among the five projects and is of excellent grade. This is followed by the Hongfeng photovoltaic project, the De Aar wind power project, and the Yamal liquefied natural gas project, which are of good grade. The Lamu coal power station project has the worst green governance and is at a medium level.

This study can assist Belt and Road energy projects in identifying their deficiencies and promoting sustainable development by providing a robust framework for green governance evaluation.

The indicator system developed in this study includes social and project governance aspects in addition to environmental performance, reflecting the comprehensive green governance status of projects. The combined use of ANP and grey system theory fully considers the mutual influence relationship between indicators and improves the objectivity of green governance grade judgment.