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Online shoppers make product purchase decisions based on product information shown on a retailer's website and potentially in comparison to that seen on competitors' websites. Insufficient, poor quality or missing information about a product can lead to reduced retailer sales. Measuring online product information quality (PIQ) is therefore an essential element in helping retailers maximize their potential success. This paper aims (1) to identify directly quantifiable PIQ criteria, (2) to assess the effects of PIQ and (3) to evaluate the moderating effect of product involvement.

The authors conducted a scenario-based experiment within 3,544 do-it-yourself (DIY) online shoppers from the United Kingdom (UK). Within an 8 × 2 × 2 between-subjects design, the authors manipulated the factors PIQ criteria (8), PIQ level (2) and product type (2).

The findings support that poor PIQ has a negative impact on consumers online shopping outcomes. The authors also found that the effects of PIQ differ between the various criteria, the product category and the level of consumer involvement in the selling process. In the context of product depiction, title readability and product attribute comparability with other retailers' websites a high level of PIQ is required. Moreover, high involvement products need a higher level of PIQ than low involvement products.

This research expands website quality and service failure literature by introducing PIQ criteria and its effects in the context of online retailing. The authors also establish actionable managerial recommendations to assist retailers to embrace and utilize PIQ to better understand their own potential website and thus business improvements.

The existing literature expresses a strong need to develop tools that support the manufacturing reshoring decision-making process. This paper aims to examine the suitability of analytical hierarchy process (AHP)-based tools for initial screening of manufacturing reshoring decisions.

Two AHP-based tools for the initial screening of manufacturing reshoring decisions are developed. The first tool is based on traditional AHP, while the second is based on fuzzy-AHP. Six high-level and holistic reshoring criteria based on competitive priorities were identified through a literature review. Next, a panel of experts from a Swedish manufacturing company was involved in the overall comparison of the criteria. Based on this comparison, priority weights of the criteria were obtained through a pairwise analysis. Subsequently, the priority weights were used in a weighted-sum manner to evaluate 20 reshoring scenarios. Afterwards, the outputs from the traditional AHP and fuzzy-AHP tools were compared to the opinions of the experts. Finally, a sensitivity analysis was performed to evaluate the stability of the developed decision support tools.

The research demonstrates that AHP-based support tools are suitable for the initial screening of manufacturing reshoring decisions. With regard to the presented set of criteria and reshoring scenarios, both traditional AHP and fuzzy-AHP are shown to be consistent with the experts' decisions. Moreover, fuzzy-AHP is shown to be marginally more reliable than traditional AHP. According to the sensitivity analysis, the order of importance of the six criteria is stable for high values of weights of cost and quality criteria.

The limitation of the developed AHP-based tools is that they currently only include a limited number of high-level decision criteria. Therefore, future research should focus on adding low-level criteria to the tools using a multi-level architecture. The current research contributes to the body of literature on the manufacturing reshoring decision-making process by addressing decision-making issues in general and by demonstrating the suitability of two decision support tools applied to the manufacturing reshoring field in particular.

This research provides practitioners with two decision support tools for the initial screening of manufacturing reshoring decisions, which will help managers optimize their time and resources on the most promising reshoring alternatives. Given the complex nature of reshoring decisions, the results from the fuzzy-AHP are shown to be slightly closer to those of the experts than traditional AHP for initial screening of manufacturing relocation decisions.

This paper describes two decision support tools that can be applied for the initial screening of manufacturing reshoring decisions while considering six high-level and holistic criteria. Both support tools are applied to evaluate 20 identical manufacturing reshoring scenarios, allowing a comparison of their output. The sensitivity analysis demonstrates the relative importance of the reshoring criteria.

Nowadays businesses tend to compete with rivals by improving capability to meet customer demands. One of the key to improve logistics efficiency of a firm is to select appropriate supplier. In the past, to select the most suitable supplier, most people evaluated performance by using average performance or variance from historical data but did not mentioned skewness. In other words, skewness impact on supplier performance is ignored by researchers and buyers. In fact, supplier with greatest average performance does not confirm to be the most suitable one because of uncertainties which make its performance skew either to the left or right, i.e., lower or higher than expectation. Therefore, this empirical study aims to discover and determine the important role of skewness on supplier selection problem. After identifying influential criteria on supplier selection, we analyze skewness effect on suppliers’ performance in each criterion by surveying real data of suppliers’ performances. Skewness effect can be rated in 3 levels; no effect, moderately effect, and highly effect. The results show that, there is only one criterion with no skewness effect, which is price. Criteria which have high skewed performance, for both of medium-sized and large-sized buyers, are lead time, product quality and reliability, and on-time delivery. Also, skewness has higher effect on suppliers’ performance of medium-sized buyers than large-sized buyers. The conclusion surprisingly shows that, skewness is the best index to distinguish between good and bad suppliers, while mean is the worst index.

Conducting research that is both practice- and theory-relevant is important for the service research community. Action research can be a fruitful approach for service researchers studying the transformative role of service research and wanting to make contributions to both the research community and to practical development. By exploring the current use of action research in service research, this study aims to make suggestions for enhancing the contribution to theory and practice development and to propose criteria for research quality for action research in service research.

This study builds on a systematic literature review of the use of action research approaches in service research.

The study makes three main contributions. First, it posits that any action research project needs to consider the four elements of problem identification, theorization, creating guiding concepts and intervention. Second, based on these elements mirrored in service action research, it outlines and analyzes three approaches to action research (i.e. theory-enhancing, concept developing and practice-enhancing). Third, it suggests a move from instrumental to a more conceptual relevance of the research and elaborates on the criteria for research quality.

This study contributes to the understanding of how action research may be applied for conducting high-quality collaborative research in services and proposes measures to enhance research quality in action research projects focusing services.

This study aims to identify clusters amongst the county housing markets in Poland, taking into account the criteria of size and quality of the housing stock, as well as price level. In addition, this work is intended to detect the socio-economic factors driving the cluster formation.

To group the studied housing markets into homogeneous clusters, this analysis uses a proprietary algorithm based on taxonomic and k-means++ methods. In turn, the generalised ordered logit (gologit) model was used to explore factors influencing the cluster formation.

The results obtained revealed that Polish county housing markets can be classified into three or four homogeneous clusters in terms of the size and quality of the housing stock and price level. Furthermore, the results of the estimation of the gologit models indicated that population density, number of business entities and the level of crime mainly determine the membership of a given housing market in a given cluster.

In contrast to previous studies, this is the first to examine the existence of homogeneous clusters amongst the county housing markets in Poland, taking into account the criteria of size and quality of the housing stock, as well as price level simultaneously. Moreover, this work is the first to identify the driving forces behind the formation of clusters amongst the surveyed housing markets.

Quality assurance (QA) in open and distance learning (ODL) has always become universal concerns of stakeholders. The quality of ODL has been confronted with challenges in terms of the diversity of inputs, processes, the complex supply chain management of ODL and recent paradigm shift into online learning. Assuring the quality of ODL are daunting tasks at individual, institution and system levels. Completed before the beginning of the COVID-19 outbreak, this study aims to better understand the implementation of QA system in three Asian open universities (OUs), namely University of the Philippines Open University (UPOU), Universitas Terbuka (UT), Indonesia and Allama Iqbal Open University (AIOU), Pakistan.

A qualitative method was employed involving analysis of documents of the three Asian OUs and focus group discussions and interviews with management and staff. Data collected were then analyzed to draw conclusions and possible recommendations.

Findings of this study presented good practices, challenges and rooms for improvement of the QA system in the three Asian OUs. Focusing on students and stakeholders in their QA effort, this study has revealed that quality begins with inner self and is multidimensional. QA is principally viewed as continuous improvement, as mechanism and assessment and as effort at exceeding expectations of students and stakeholders. The recent challenge for QA is to embrace a delicate process of ODL transformation into online digital system. The recent COVID-19 outbreak has further implications and challenged QA implementation in ODL in higher education into the next level of complexity.

This study revealed the diversities in how OUs met the societal needs of their respective stakeholders and addressed the challenges ahead for QA in ODL.

These findings were expected to enhance the understanding of the theory and practice of QA in ODL and to contribute to quality improvement of ODL programs.

This paper investigates the suitability of fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

Two fuzzy-logic-based support tools are developed together with experts from a Swedish manufacturing firm. The first uses a complete rule base and the second a reduced rule base. Sixteen inference settings are used in both of the support tools.

The findings show that fuzzy-logic-based support tools are suitable for initial screening of manufacturing reshoring decisions. The developed support tools are capable of suggesting whether a reshoring decision should be further evaluated or not, based on six primary competitiveness criteria. In contrast to existing literature this research shows that it does not matter whether a complete or reduced rule base is used when it comes to accuracy. The developed support tools perform similarly with no statistically significant differences. However, since the interpretability is much higher when a reduced rule base is used and it require fewer resources to develop, the second tool is more preferable for initial screening purposes.

The developed support tools are implemented at a primary-criteria level and to make them more applicable, they should also include the sub-criteria level. The support tools should also be expanded to not only consider competitiveness criteria, but also other criteria related to availability of resources and strategic orientation of the firm. This requires further research with regard to multi-stage architecture and automatic generation of fuzzy rules in the manufacturing reshoring domain.

The support tools help managers to invest their scarce time on the most promising reshoring projects and to make timely and resilient decisions by taking a holistic perspective on competitiveness. Practitioners are advised to choose the type of support tool based on the available data.

There is a general lack of decision support tools in the manufacturing reshoring domain. This paper addresses the gap by developing fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

To address requirements and specifications of construction project, academics need to build a project classification model. In recent years, project success concept, particularly on large-scale construction projects, has been a controversial issue, especially in developing countries. Hence, in this paper, after introducing a sustainable success index (SSI), a novel method called “rough set approach” had been adopted to induce decision rules and to classify construction projects. The paper aims to discuss these issues.

At first, 20 effective success factors and 15 success criteria based on three pillars of sustainability of economy, society and environment had been categorized. The research data used for analysis had been collected from 26 large-scale construction projects in Iran and five other countries. After collecting data collection, observations had been analyzed and 51 decision rules were generated, and the projects were classified. Eventually, in order to evaluate the performance of the generated rules, confusion matrix was applied, and the model was validated.

The results of the present study show that rough set theory (RST) can be an effective and valuable tool for building expert systems. Practical applications of these results along with limitations and future research are described.

Perhaps for the first time, in the present study, a number of large-scale construction projects are classified based on SSI. Applying RST for building rule-based system and classifying projects in construction project area are novel attempts undertaken in this paper. The rules induced in this study can be applied to develop a sustainable success prediction model in the future studies.

This study aims to examine the moderating effect of internal control on the relationship between internal quality assurance implementation and higher education institution (HEI) quality.

The study employed a survey method with hypothesis testing using partial least squares-structural equation modelling (PLS-SEM) technique with a second-order analysis approach. The questionnaire survey was administered to 191 HEIs.

The results revealed that internal control and internal quality assurance implementations are positively associated with HEI quality. Additionally, internal control implementation strengthened the relationship between internal quality assurance and HEI quality.

This study adds to the body of knowledge on the moderating role of internal control in the HEI sector and its role in the non-economic aspect, namely, the HEI quality.