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Article
Publication date: 1 August 1991

Kun‐Huang Yeh and Chao‐Hsien Chu

Under acute global competitive pressure, many companies have viewedproduct diversification as a strategic weapon with which to win marketshares. Nevertheless, product…

514

Abstract

Under acute global competitive pressure, many companies have viewed product diversification as a strategic weapon with which to win market shares. Nevertheless, product diversification increases costs and sometimes degrades product quality. Many businesses today confront the dilemma of whether to broaden product lines or to focus production. This article explores both theoretically and empirically the possible impacts of product diversification. The emerging technologies and adaptive manufacturing strategies, potentially meeting a variety of customer needs while enhancing other competitive advantages, are examined.

Details

International Journal of Operations & Production Management, vol. 11 no. 8
Type: Research Article
ISSN: 0144-3577

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Article
Publication date: 12 August 2014

Yu-Ting Cheng and Chih-Ching Yang

Constructing a fuzzy control chart with interval-valued fuzzy data is an important topic in the fields of medical, sociological, economics, service and management. In particular…

309

Abstract

Purpose

Constructing a fuzzy control chart with interval-valued fuzzy data is an important topic in the fields of medical, sociological, economics, service and management. In particular, when the data illustrates uncertainty, inconsistency and is incomplete which is often the. case of real data. Traditionally, we use variable control chart to detect the process shift with real value. However, when the real data is composed of interval-valued fuzzy, it is not feasible to use such an approach of traditional statistical process control (SPC) to monitor the fuzzy control chart. The purpose of this paper is to propose the designed standardized fuzzy control chart for interval-valued fuzzy data set.

Design/methodology/approach

The general statistical principles used on the standardized control chart are applied to fuzzy control chart for interval-valued fuzzy data.

Findings

When the real data is composed of interval-valued fuzzy, it is not feasible to use such an approach of traditional SPC to monitor the fuzzy control chart. This study proposes the designed standardized fuzzy control chart for interval-valued fuzzy data set of vegetable price from January 2009 to September 2010 in Taiwan obtained from Council of Agriculture, Executive Yuan. Empirical studies are used to illustrate the application for designing standardized fuzzy control chart. More related practical phenomena can be explained by this appropriate definition of fuzzy control chart.

Originality/value

This paper uses a simpler approach to construct the standardized interval-valued chart for fuzzy data based on traditional standardized control chart which is easy and straightforward. Moreover, the control limit of the designed standardized fuzzy control chart is an interval with (LCL, UCL), which consists of the conventional range of classical standardized control chart.

Details

Management Decision, vol. 52 no. 7
Type: Research Article
ISSN: 0025-1747

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Article
Publication date: 12 November 2019

Kun-Huang Huarng and Tiffany Hui-Kuang Yu

The use of linear regression analysis is common in the social sciences. The purpose of this paper is to show the advantage of a qualitative research method, namely, structured…

997

Abstract

Purpose

The use of linear regression analysis is common in the social sciences. The purpose of this paper is to show the advantage of a qualitative research method, namely, structured qualitative analysis (SQA), over the linear regression method by using different characteristics of data.

Design/methodology/approach

Data were gathered from a study of online consumer behavior in Taiwan. The authors changed the content of the data to have different sets of data. These data sets were used to demonstrate how SQA and linear regression works individually, and to contrast the empirical analyses and empirical results from linear regression and SQA.

Findings

The linear regression method uses one equation to model different characteristics of data. When facing a data set containing a big and a small size of different characteristics, linear regression tends to provide an equation by modeling the characteristics of the big size data and subsuming those of the small size. When facing a data set containing similar sizes of data with different characteristics, linear regression tends to provide an equation by averaging these data. The major concern is that the one equation may not be able to reflect the data of various characteristics (different values of independent variables) that result in the same outcome (the same value of dependent variable). In contrast, SQA can identify various variable combinations (multiple relationships) leading to the same outcome. SQA provided multiple relationships to represent different sizes of data with different characteristics so it created consistent empirical results.

Research limitations/implications

Two research methods work differently. The popular linear regression tends to use one equation to model different sizes and characteristics of data. The single equation may not be able to cover different behaviors but may lead to the same outcome. Instead, SQA provides multiple relationships for different sizes of data with different characteristics. The analyses are more consistent and the results are more appropriate. The academics may re-think the existing literature using linear regression. It would be interesting to see if there are new findings for similar problems by using SQA. The practitioners have a new method to model real world problems and to understand different possible combinations of variables leading to the same outcome. Even the relationship obtained from a small data set may be very valuable to practitioners.

Originality/value

This paper compared online consumer behavior by using two research methods to analyze different data sets. The paper offered the manipulation of real data sets to create different data sizes of different characteristics. The variations in empirical results from both methods due to the various data sets facilitate the comparison of both methods. Hence, this paper can serve as a complement to the existing literature, focusing on the justification of research methods and on limitations of linear regression.

Details

International Journal of Emerging Markets, vol. 15 no. 4
Type: Research Article
ISSN: 1746-8809

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Article
Publication date: 4 September 2024

Holly Hapke, Anita Lee-Post, Tereza Dean and Kun Huang

We propose and test a theoretically grounded structural model of our postulation, providing insights into how students’ COVID experiences affect their general learning…

35

Abstract

Purpose

We propose and test a theoretically grounded structural model of our postulation, providing insights into how students’ COVID experiences affect their general learning experiences, specific learning experiences and specific class performance post-COVID.

Design/methodology/approach

Numerous studies have reported how COVID-19 has impacted student learning in higher education during the pandemic from Spring 2020 to Fall 2021. Students were found to be disengaged, unmotivated, isolated, anxious, having difficulty with emergency remote teaching (ERT) and dealing with financial, physical and/or mental health issues. Against this backdrop, we conduct our own investigation to ascertain what the lasting impacts of COVID-19 are on student learning using a survey instrument. Specifically, we postulate that the academic and social disruptions of COVID-19 affected students’ social connectedness and mental well-being, which in turn, affected their affective and cognitive learning outcomes. We used structural equation modeling to validate a structural model grounded in self-determination theory that capatures the complex relationships between genaral and specfiic COVID-19 impact factors on student learning as seven hypotheses.

Findings

All seven of our hypotheses are supported suggesting that both class-specific factors and broader general factors beyond the classroom affect student's satisfaction with and learning in the class, as postulated in our structural model.

Originality/value

We advance the work of self-determination theory by conceptualizing and modeling the roles that all three self-determination needs play in investigating COVID-19's impact on learning. Overall, our study confirms the value of considering affective factors such as anxiety and satisfaction in learning research.

Details

Journal of Applied Research in Higher Education, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-7003

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