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The purpose of this paper is to investigate factors that influence effective workplace designs for knowledge workers.

During spring 2016, the employees in a large institution for research and higher education, a large consultancy company and a medium-sized consultancy company (in total 4367 employees) in Norway received invitations to participate in an anonymous online survey about workplaces and facilities. In all, 1,670 employees answered the survey (38.2 per cent response rate). The data have been analyzed with IBM SPSS version 23, among others through use of exploratory factor analysis and two-way ANOVA.

Most respondents at the institution for research and higher education have cell offices. Most respondents in the two consultancy companies have open and flexible offices. This paper indicate the respondents’ preferences or perception of their workstation and the workplace’s fit for their tasks is affected both by the respondents’ type of office and how much time they spend at their workstation during the week. There are also possible age or generation effects.

One methodical weakness in the present paper is that two-way ANOVA has been applied on survey data. Experiments are usually arranged to provide almost equal numbers of observations in each category. This is usually not possible with survey data. However, despite this weakness, the present paper provides several findings that challenge some of the workplace research’s taken for givens.

The present paper indicates that facility managers and others responsible for office and workplace design are advised to take the employees’ tasks and work patterns into consideration when designing workplaces and providing offices and workstations to their end-users. The present paper also indicates that employees require different kinds of support facilities and services depending on what kind of offices and workplaces they have.

This is a large N empirical study among knowledge workers in three organizations, one public administration and two private enterprises. The present paper indicate that provision of offices and workstations with supporting facilities should be differentiated according to the end-users’ work tasks and work patterns.

Describes multiple comparison procedures applied to three or more sample groups after ANOVA. Illustrates ANOVA applied to experiments with completely randomised design and randomised block design.

The purpose of this article is to study the link between mean shift and inflation coefficient when the underlying null hypothesis is rejected in the analysis of variance (ANOVA) application after the preliminary test on the model specification.

A new approach is proposed to study the link between mean shift and inflation coefficient when the underlying null hypothesis is rejected in the ANOVA application. First, we determine this relationship from the general perspective of Six Sigma methodology under the normality assumption. Then, the approach is extended to a balanced two-stage nested design with a random effects model in which a preliminary test is used to fix the main test statistic.

The features of mean-shifted and inflated (but centred) processes with the same specification limits from the perspective of Six Sigma are studied. The shift and inflation coefficients are derived for the two-stage balanced ANOVA model. We obtained good predictions for the process shift, given the inflation coefficient, which has been demonstrated using numerical results and applied to case studies. It is understood that the proposed method may be used as a tool to obtain an efficient variance estimator under mean shift.

In this work, as a new research approach, we studied the link between mean shift and inflation coefficients when the underlying null hypothesis is rejected in the ANOVA. Derivations for these coefficients are presented. The results when the null hypothesis is accepted are also studied. This needs the help of preliminary tests to decide on the model assumptions, and hence the researchers are expected to be familiar with the application of preliminary tests.

After studying the proposed approach with extensive numerical results, we have provided two practical examples that demonstrate the significance of the approach for real-time practitioners. The practitioners are expected to take additional care before deciding on the model assumptions by applying preliminary tests.

The proposed approach is original in the sense that there have been no similar approaches existing in the literature that combine Six Sigma and preliminary tests in ANOVA applications.

The purpose of this paper is to investigate the vehicle-based sensor effect and pavement temperature on road condition assessment, as well as to compute a threshold value for the classification of pavement conditions.

Four sensors were placed on the vehicle’s control arms and one inside the vehicle to collect vibration acceleration data for analysis. The Analysis of Variance (ANOVA) tests were performed to diagnose the effect of the vehicle-based sensors’ placement in the field. To classify road conditions and identify pavement distress (point of interest), the probability distribution was applied based on the magnitude values of vibration data.

Results from ANOVA indicate that pavement sensing patterns from the sensors placed on the front control arms were statistically significant, and there is no difference between the sensors placed on the same side of the vehicle (e.g., left or right side). A reference threshold (i.e., 1.7 g) was computed from the distribution fitting method to classify road conditions and identify the road distress based on the magnitude values that combine all acceleration along three axes. In addition, the pavement temperature was found to be highly correlated with the sensing patterns, which is noteworthy for future projects.

The paper investigates the effect of pavement sensors’ placement in assessing road conditions, emphasizing the implications for future road condition assessment projects. A threshold value for classifying road conditions was proposed and applied in class assignments (I-17 highway projects).

This paper deals with the 1‐Way Fuzzy ANOVA and compares it with other classical techniques. Difficulties in obtaining general theoretic results force one to use simulation techniques. A variety of situations is considered in order to validate the conclusions.

This study explores how titanium oxide (TiO₂) filler influences the specific wear rate (SWR) in flax fiber-reinforced epoxy composites (FFRCs) through a Taguchi approach. It aims to boost abrasive wear resistance by incorporating TiO₂ filler, promoting sustainable and eco-friendly materials.

This study fabricates epoxy/flax composites with TiO₂ particles (0–8 wt%) using hand layup. Composites were tested for wear following American Society for Testing and Materials (ASTM) G99-05. Statistical analysis used Taguchi design of experiments (DOE), with ANOVA identifying key factors affecting SWR in abrasive sliding conditions.

The study illuminates how integrating TiO2 filler particles into epoxy/flax composites enhances abrasive wear properties. Statistical analysis of SWR highlights abrasive grit size (grit) as the most influential factor, followed by normal load, wt% of TiO₂ and sliding distance. Grit size has the highest effect at 43.78%, and wt% TiO₂ filler contributes 15.61% to SWR according to ANOVA. Notably, the Taguchi predictive model closely aligns with experimental results, validating its reliability.

This paper integrates TiO₂ filler and flax fibers to form a novel hybrid composite with enhanced tribological properties in epoxy composites. The use of Taguchi DOE and ANOVA offers valuable insights for optimizing control variables, particularly in natural fiber-reinforced composites (NFRCs).

The purpose of this paper is to examine the role of reading in scholarly work among academics in Finland. This study analyzes readings from a variety of publication types including books, conference proceedings, research reports, magazines, newspapers, blogs, non-fiction and fiction.

An online survey was developed and distributed in Finland in 2016–2017 (n=528). Participants were asked their finding and use of scholarly information resources of all types.

Scholars read from a variety of publications. Different types of publications are read and used differently. Reading also varies between disciplines, ranks, work responsibilities and type of research performed.

The study was a nationwide study of researchers in Finland; therefore, all findings are within the context of researchers in a single country. All results are self-reported; therefore, the authors assume but cannot be sure that respondents accurately recollect the specifics of their use of scholarly information.

The results of this study are relevant to publishers, research librarians, editors and others who serve consumers of scholarly information resources, design information products and services for those scholars, and seek to better understand the information needs and use of a variety of types of scholarly publications.

This study replicates previous studies in a variety of countries and provides a more up-to-date and single-country contextualized overview of how researchers find and use scholarly information in their work.

The purpose of this paper is a comparable evaluation of the influence of a particular element (Bi and Sb) added to Sn‐Ag‐Cu and Sn‐Zn alloys on their surface and interfacial tensions, as well as the wetting properties on the Cu substrate expressed by the wetting angle.

The authors applied the L₈ orthogonal Taguchi array to carry out the experiments and discussed the results using analysis of variance (ANOVA).

It was expected, on the base of previous studies, the decrease of the surface and interfacial tensions and thus improving wettability after the Bi and Sb addition to Sn‐Ag‐Cu and Sn‐Zn alloys. Unfortunately, the obtained results on the quinary Sn‐Ag‐Cu‐Bi‐Sb alloys and the quaternary Sn‐Zn‐Bi‐Sb alloys do not confirm these trends. The performed analyses suggest that the compositions of the quinary Sn‐Ag‐Cu‐Bi‐Sb alloys, as well as the quaternary Sn‐Zn‐Bi‐Sb alloys, do not have optimal compositions for practical application. The Cu, Bi and Sb elements in the case of the Sn‐Ag‐Cu‐Bi‐Sb alloys and the Zn, Bi and Sb elements in the case of the Sn‐Zn‐Bi‐Sb alloys show mutual interaction and, in consequence, there is no correlation between the tendency of the surface and interfacial tensions changes and the wettings of the Cu substrate.

It is suggested that further studies are necessary for the purpose of the practical application, but they should be limited mainly to the Sn‐Ag‐Cu‐Bi and the Sn‐Zn‐Bi alloys with the optimal compositions.

The performed analysis suggests that none of the investigated compositions of the quinary Sn‐Ag‐Cu‐Bi‐Sb alloys, as well as the quaternary Sn‐Zn‐Bi‐Sb alloys, have the optimal compositions for practical application.

The quickest way to determine which element of the alloy composition influences the surface tension and the wetting properties, and how, is to apply orthogonal analysis. After choosing the orthogonal array, the experiments were performed and analysis of variance (ANOVA) was used to perform the quantifiable analysis of the measured and calculated results of surface and interfacial tensions, as well as the wetting properties on the Cu substrate.

Describes non‐parametric methods applied to comparisons of three or more sample groups.Illustrates non‐parametric ANOVA applied to a randomized block design for a consumer sensory experiment.

The purpose of this paper is to determine the significant variables leading to successful implementation of enterprise resource management (ERM) and its predecessor concepts of enterprise resource planning, supply chain planning, electronic commerce, and radio frequency identification systems. An implementation roadmap is presented using four stages for implementing ERM systems: planning, development, implementation, and testing. The roadmap indicates when and where the significant success variables would appear and how a firm might manage the implementation process.

In the research, the key success criteria and key implementation drivers uncovered by literature, case studies, and interviews were used. A survey instrument was constructed and the survey hosted on a web site where practitioners from industry were invited to supply opinions. The data were analyzed by using correlation models and one‐way analysis of variance (ANOVA) to develop cause‐effect diagrams (CE) for overall ERM systems and each component of ERM. Based on the controlled error of the ANOVA process, the CE diagram was used to depict the degree to which variables influence implementation success.

The research results have helped uncover the key significant variables that contribute to successful ERM implementation.

The proposed implementation roadmap indicates when and where the significant success variables would appear and how a firm might manage the implementation process.