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This paper aims to introduce the weighted squared Euclidean distance between points in signal space, to improve the performance of the Wi-Fi indoor positioning. Nowadays, the received signal strength-based Wi-Fi indoor positioning, a low-cost indoor positioning approach, has attracted a significant attention from both academia and industry.

The local principal gradient direction is introduced and used to define the weighting function and an average algorithm based on k-means algorithm is used to estimate the local principal gradient direction of each access point. Then, correlation distance is used in the new method to find the k nearest calibration points. The weighted squared Euclidean distance between the nearest calibration point and target point is calculated and used to estimate the position of target point.

Experiments are conducted and the results indicate that the proposed Wi-Fi indoor positioning approach considerably outperforms the weighted k nearest neighbor method. The new method also outperforms support vector regression and extreme learning machine algorithms in the absence of sufficient fingerprints.

Weighted k nearest neighbor approach, support vector regression algorithm and extreme learning machine algorithm are the three classic strategies for location determination using Wi-Fi fingerprinting. However, weighted k nearest neighbor suffers from dramatic performance degradation in the presence of multipath signal attenuation and environmental changes. More fingerprints are required for support vector regression algorithm to ensure the desirable performance; and labeling Wi-Fi fingerprints is labor-intensive. The performance of extreme learning machine algorithm may not be stable.

The new weighted squared Euclidean distance-based Wi-Fi indoor positioning strategy can improve the performance of Wi-Fi indoor positioning system.

The received signal strength-based effective Wi-Fi indoor positioning system can substitute for global positioning system that does not work indoors. This effective and low-cost positioning approach would be promising for many indoor-based location services.

A novel Wi-Fi indoor positioning strategy based on the weighted squared Euclidean distance is proposed in this paper to improve the performance of the Wi-Fi indoor positioning, and the local principal gradient direction is introduced and used to define the weighting function.

According to the grey uncertainty and the connotation of different types weights, the purpose of this paper is to establish the pattern of multi-dimensional grey fuzzy decision making with feedback based on weight vector and weight matrix, and applies this pattern to evaluate the regional financial innovation ability.

At first, this paper analyzes the connotation of financial innovation ability and establishes the evaluation system of regional financial innovation ability. Second, the formula of computing the multi-objective weighted comprehensive value based on weight vector and weight matrix is put forward. In view of the object function with supervised factor and stability coefficient, this paper gives the formulas to compute weight vector and weight matrix. Moreover, the algorithm of the multi-dimensional grey fuzzy decision making pattern with feedback based on weight vector and weight matrix is expressed. At last, this paper uses the presented pattern to evaluate the financial innovation ability of thirty-one provinces in China.

The results are convincing: the development of regional financial innovation is not balanced in China, having obvious spatial clustering feature. The comparisons of evaluation results based on different forms of weights show that the calculating convergence speed of the pattern presented in this paper is fast. The pattern enhances the rationality of the demarcation point between categories, and the convergence within categories, making the evaluation more reasonable.

The method exposed in the paper can be used at evaluating the regional financial innovation ability and even for other similar evaluation problem.

The paper succeeds in realising both the pattern of multi-dimensional grey fuzzy decision making with feedback and evaluating the regional financial innovation ability by using the newest developed theories: weighted grey and fuzzy recognition theory based on weight vector and weight matrix.

The study demonstrates the viability of situational segmentation in a market outside the USA. A number of situational segmentation studies in the USA have examined the snacking market. This study examines situational segmentation opportunities in the context of the Japanese snacking market. The study attempts to delineate a situationally‐defined market structure for a broadly defined array of snack products. This is done by characterizing 18 snacks in terms of pertinent situational factors via dummy variable regression analysis; and grouping the snacks in terms of the similarity of their situational characterizations via cluster analysis. The study reveals four multi‐product snack segments, including solitary snacking cluster, socializing ensemble cluster, high gravity socialization cluster, and morning home snack. The results show that situational segmentation is as effective in complementing more traditional segmentation approaches in Japan as it is in the USA.

People seem to function according to different models, which implies that in business and social sciences, heterogeneity is a rule rather than an exception. Researchers can investigate such heterogeneity through multigroup analysis (MGA). In the context of partial least squares path modeling (PLS-PM), MGA is currently applied to perform multiple comparisons of parameters across groups. However, this approach has significant drawbacks: first, the whole model is not considered when comparing groups, and second, the family-wise error rate is higher than the predefined significance level when the groups are indeed homogenous, leading to incorrect conclusions. Against this background, the purpose of this paper is to present and validate new MGA tests, which are applicable in the context of PLS-PM, and to compare their efficacy to existing approaches.

The authors propose two tests that adopt the squared Euclidean distance and the geodesic distance to compare the model-implied indicator correlation matrix across groups. The authors employ permutation to obtain the corresponding reference distribution to draw statistical inference about group differences. A Monte Carlo simulation provides insights into the sensitivity and specificity of both permutation tests and their performance, in comparison to existing approaches.

Both proposed tests provide a considerable degree of statistical power. However, the test based on the geodesic distance outperforms the test based on the squared Euclidean distance in this regard. Moreover, both proposed tests lead to rejection rates close to the predefined significance level in the case of no group differences. Hence, our proposed tests are more reliable than an uncontrolled repeated comparison approach.

Current guidelines on MGA in the context of PLS-PM should be extended by applying the proposed tests in an early phase of the analysis. Beyond our initial insights, more research is required to assess the performance of the proposed tests in different situations.

This paper contributes to the existing PLS-PM literature by proposing two new tests to assess multigroup differences. For the first time, this allows researchers to statistically compare a whole model across groups by applying a single statistical test.

This paper aims to propose a new benchmarking framework that uses a series of existing intuitive and analytical methods to systematically capture both objective data and subjective beliefs and preferences from a group of decision makers (DMs).

The proposed framework combines the excellence model developed by the European Foundation for Quality Management with the Rembrandt method, the entropy concept, the weighted‐sum approach, and the theory of the displaced ideal. Hard data and personal judgments are synthesized to evaluate a set of business units (BUs) with two overall performance scores plotted in a four quadrant model.

The two performance scores are used to benchmark the performance of the BUs in accordance with their Euclidean distance from the “ideal” BU. Quadrants are used to classify the BUs as efficacious, productive ineffectual, proficient unproductive, and inefficacious. The efficacious BUs, referred to as “excellent”, fall in the competency zone and have the shortest Euclidean distance from the ideal BU relative to their peers.

The benchmarking framework presented in this study has some obvious attractive features. First, the generic nature of the framework allows for the subjective and objective evaluation of a finite number of BUs by a group of DMs. Second, the information requirements of the framework are stratified hierarchically allowing DMs to focus on a small area of the large problem. Third, the framework does not dispel subjectivity; it calibrates the subjective weights with the objective weights determined through the entropy concept.

The aim of this paper is to investigate whether buyers and sellers appears to take distance to the capital business district (CBD) into account in their valuation for acquisition or disposition.

Under a mono‐centric model conceptualization, applicable to the central area of many European cities, location can be represented by distance to the city center. The effect of several distance measures on selling price is investigated for income properties with mixed residential and commercial components – geometric distances, driving distance and time, and time by subway. Exponential and multiplicative models are considered and estimated using a robust estimation method.

The findings indicate the mono‐centric model to be a useful conceptualization, that buyers and sellers of income properties do take distance into account, and that a number of buildings operate under a suboptimal split between residential and commercial components.

In social science even when a model shows good statistical fit, one will not know if it is correct, but only that it represents observed relationships well. Several models, however, may fit the data. The authors chose ones used by other researchers in similar investigations in past decades. Variable definition and measurement are always issues. In the present case “time by subway” is a mix of walking‐waiting‐riding and minutes in each activity would not be equivalent, involving inter‐personal comparisons of utility. The variable “effective age” based upon the assessment concept of “value year” may not fully capture age‐related effects on price.

The key implications are that the multiplicative model may well be a suitable functional form for these types of analyses and that robust methods are important to prevent outliers in the data from having an undue influence on the estimation.

The authors used robust estimation methods for the price models. The authors defined and studied a sub‐optimality ratio of residential area to commercial area in the mixed use buildings.

Due to such issues as the recent economic recession, low salaries, and an aging society, how people can strengthen their investment performance when managing their personal financial affairs is a critical consideration. The purpose of this paper is to consider the assessment of the performance of individual investment policies and to present an evaluation framework for measuring the degree of workability of investment policies.

The proposed evaluation framework combines the fuzzy analytical hierarchy process and the improved fuzzy technique for order preference by similarity to the ideal solution to measure the efficiency scores of the alternatives (i.e. investment policies) under assessment.

This quantitative framework is formed from the criteria of investment return, taxation, risk, and individual circumstances according to prudent evaluation of private wealth management research, and is applied to appraise the investment performance of individuals in Taiwan. The findings indicate that investment performance, risks, and the investment of mutual funds are the most preferred conditions and investment policy for investors, and can offer some effective suggestions for investors as well as for future academic research.

The efficiency scores are computed based on the fuzzy Mahalanobis distances, taking into account the fuzzy correlations among experts’ criteria. The advantage of adopting the fuzzy Mahalanobis distances over the fuzzy Euclidean distances, which are typically computed in the literature, is that the undulation of the efficiency scores can be reduced.

At present, smartphones are embedded with accelerometers, gyroscopes, magnetometers and WiFi sensors. Most researchers have delved into the use of these sensors for localization. However, there are still many problems in reducing fingerprint mismatching and fusing these positioning data. The purpose of this paper is to improve positioning accuracy by reducing fingerprint mismatching and designing a weighted fusion algorithm.

For the problem of magnetic mismatching caused by singularity fingerprint, derivative Euclidean distance uses adjacent fingerprints to eliminate the influence of singularity fingerprint. To improve the positioning accuracy and robustness of the indoor navigation system, a weighted extended Kalman filter uses a weighted factor to fuse multisensor data.

The scenes of the teaching building, study room and office building are selected to collect data to test the algorithm’s performance. Experiments show that the average positioning accuracies of the teaching building, study room and office building are 1.41 m, 1.17 m, and 1.77 m, respectively.

The algorithm proposed in this paper effectively reduces fingerprint mismatching and improve positioning accuracy by adding a weighted factor. It provides a feasible solution for indoor positioning.