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Explores the interval nature of semantic scale adjectives across three languages: English, Putonghua Chinese, and Japanese. Reports on a pilot study conducted among native speakers of each language using the techniques of magnitude scaling. Respondents rated an assortment of common adjectives by comparing the magnitude of the word to a given modulus. The results indicate that the traditional translation/back‐translation technique may not provide response intervals that are comparable cross‐culturally. Further, between languages the results indicate that the meaning attached to the adjectives by native speakers varies substantially. Discusses implications for market research, as well as future areas of research.

This paper is the main section on quantitative data analysis. It explains the concepts at a greater detail to help non-Math/Stat scholars to understand the basics easily. Proper data analysis is critical to any research. If data are not properly analyzed, then it may give results which either cannot be properly interpreted or wrongly interpreted. This section covers univariate, multivariate analysis and then, factor analysis, cluster analysis, conjoint analysis, and multidimensional scaling (MDS) techniques.

Current practices in data classification and retrieval have experienced a surge in the use of multimedia content. Identification of desired information from the huge image databases has been facing increased complexities for designing an efficient feature extraction process. Conventional approaches of image classification with text-based image annotation have faced assorted limitations due to erroneous interpretation of vocabulary and huge time consumption involved due to manual annotation. Content-based image recognition has emerged as an alternative to combat the aforesaid limitations. However, exploring rich feature content in an image with a single technique has lesser probability of extract meaningful signatures compared to multi-technique feature extraction. Therefore, the purpose of this paper is to explore the possibilities of enhanced content-based image recognition by fusion of classification decision obtained using diverse feature extraction techniques.

Three novel techniques of feature extraction have been introduced in this paper and have been tested with four different classifiers individually. The four classifiers used for performance testing were K nearest neighbor (KNN) classifier, RIDOR classifier, artificial neural network classifier and support vector machine classifier. Thereafter, classification decisions obtained using KNN classifier for different feature extraction techniques have been integrated by Z-score normalization and feature scaling to create fusion-based framework of image recognition. It has been followed by the introduction of a fusion-based retrieval model to validate the retrieval performance with classified query. Earlier works on content-based image identification have adopted fusion-based approach. However, to the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work.

The proposed fusion techniques have successfully outclassed the state-of-the-art techniques in classification and retrieval performances. Four public data sets, namely, Wang data set, Oliva and Torralba (OT-scene) data set, Corel data set and Caltech data set comprising of 22,615 images on the whole are used for the evaluation purpose.

To the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. The novel idea of exploring rich image features by fusion of multiple feature extraction techniques has also encouraged further research on dimensionality reduction of feature vectors for enhanced classification results.

Presents some remarks about the use of rating scales in quality function deployment (QFD). Data collection and their correct interpretation are fundamental for the proper application of this tool as a support for designing activities. Particular attention is given to the critical aspects and consequences resulting from an incorrect use of rating scales. The paper illustrates how the priority rank of design characteristics can change depending on the type of scale used. Practical effects of these issues are finally shown on a real case concerning the design of a climatic control system for commercial vehicles.

This paper validates the customer contact measurement model by performing a replication using three different sample groups. The impact of customer, managerial, and cultural differences is examined. Findings indicate that all validation groups use similar variables when defining the customer contact construct. The measurement model is robust when compared to US customer and managerial validation groups. However, the applicability across culture is questioned.

Market orientation analyses have focused on two broad‐ranging approaches: the behavioural and the philosophical. The concepts of innovation and market orientation are gaining ground steadily in the context of an increasingly competitive and highly volatile environment, subject to the pressures of rapid‐changing customer needs and desires. This premise underlies the general aim of this study, which is to determine to what extent companies operating in the same sector and with similar market orientation are similarly concerned about innovation.

The population for this study comprises the leading 465 textile companies operating in Spain, listed in the ARDAN database. Data were gathered from in‐depth personalised interviews with 17 company directors operating within the textile sector. In order to verify the hypotheses, groups with similar market orientation were identified using a combination of two techniques: multidimensional scaling analysis; and cluster analysis. After this, ANOVA was used to characterise each group.

This study of the textile sector, and more specifically of its leading companies, enables one to conclude that market orientation and innovation are not isolated fields. First, four groups of firms which differ significantly in their commitment to market orientation have been found. Second, although a direct relationship between market orientation and innovation could not be statistically proved, some tools and policies considered in the innovation scale are more heavily used by the firms more orientated to the market. Third, in the context of the traditional debate in the literature about the market orientation‐performance relationship, the results of this study support a positive relationship between these two concepts.

The limitation of the sample size should lead one to treat the final results with caution.

The concepts of innovation and market orientation are gaining ground steadily in the context of an increasingly competitive and highly volatile environment, subject to the pressures of rapidly‐changing customer needs and desires. In this sense, through this paper companies can observe how these two concepts are related in a particular industry and obtain some interesting implications.

The paper reports the results of a study of food shopping behaviour in Greece. It is concerned with establishing the dimensions underlying shoppers’ evaluations of their regular supermarket store attributes, exploring the existence of shopper segments and subsequently, identifying the segments in terms of shopping behaviour and attitudes to store features. The main research instrument is a survey of adult Greek grocery shoppers in the metropolitan area of the city of Thessaloniki. The empirical results indicate that there are three dimensions that underlie the importance of store features. These are defined respectively as ‘Store design and variety’, ‘Personnel and service’, and ‘Convenient location’. The application of cluster analysis to the dimensions factor scores reveals four clusters. The characteristics of each cluster are described by average factor scores on the dimensions of store features, demographic characteristics, attitudes to store features, store loyalty, and motives for regular store choice.

The importance of analytical techniques in international marketing is illustrated. An overview of related research is given. The question is asked, “Which way will research on the subject go in the future?”

The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.

This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.

The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.

The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.

To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.

To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.

The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

The final objective of each communication action is a change in consumer behaviour. Frequently this change in attitude is translated in terms of purchases. But the change can be related to the pre‐purchase behaviour, as for instance the returning of a coupon, the ordering of a certain brochure, or a change in attitude regarding a certain product,…