Search results

The evaluation of linear dynamic response analysis of large structures by vector superposition requires, in its traditional formulation, the solution of a large and expensive eigenvalue problem. A method of solution based on a Ritz transformation to a reduced system of generalized coordinates using load dependent vectors generated from the spatial distribution of the dynamic loads is shown to maintain the high expected accuracy of modern computer analysis and significantly reduces the execution time over eigensolution procedures. New computational variants to generate load dependent vectors are presented and error norms are developed to control the convergence characteristics of load dependent Ritz solutions. Numerical applications on simple structural systems are used to show the relative efficiency of the proposed solution procedures.

Building on the transaction cost theory and power structure literature, this paper aims to investigate the extent to which firms use two safeguarding mechanisms (supply chain relational investments and electronic collaboration) in different network dependency contexts in order to protect their portfolios of business relationships.

Empirical evidence is gathered though a survey data conducted with 159 firms in the wireless communication sector. The paper tests the assumption that the two safeguarding mechanisms are used to a greater extent in interdependency‐intensive networks than in other supply chain contexts.

This empirical study suggests that: in a network‐dependent context, relational investments allow firms to safeguard their portfolios of relationships; electronic collaboration seems to be a safeguarding mechanism for firms in downstream‐dependent network contexts; in general, firms appear to use both relational investments and electronic collaboration to manage their relationships in a supply chain network; and the knowledge‐based theory may explain the strong relationship between upstream and downstream use of electronic collaboration.

Overall, the present study complements the extant literature on supply chain management and inter‐firm electronic collaboration by showing how an important structural characteristic of supply chain networks (i.e. dependency) operates on the choice of using two key safeguarding mechanisms.

Results stress the importance of these safeguarding mechanisms in joint actions such as collaborative planning, forecasting and replenishment.

The paper addresses interdependencies from a network perspective which encompasses the firms' complete portfolio of relationships.

In recent years, there has been a concern that employers are falsely classifying employees as self-employed to evade collective agreements and labour laws (e.g. minimum wages, working time legislation and protection in case of redundancy), and the result is that these dependent self-employed suffer poorer working conditions. The purpose of this paper is to provide an extensive evaluation of the working conditions of those in dependent self-employment compared with the genuine self-employed.

To do so, data are reported from a 2015 European Working Conditions Survey of 35,765 workers in 28 European Union member states.

Of the 4.3 per cent of the working population found to be in dependent self-employment, the finding is that they have similar working conditions to the genuine self-employed in terms of their physical and social environment and intensity of work. However, they have poorer job prospects and less ability to use their skills and discretion than the genuine self-employed. In terms of the working time quality, meanwhile, the finding is that they have better conditions than the genuine self-employed. Therefore, this analysis uncovers the need for a more nuanced understanding of the relative working conditions of the dependent self-employed.

If the working conditions of the dependent self-employed are to be tackled, evaluation is now required of whether the current policy approaches, such as developing a hybrid category of employment with legal rights attached, address the specific working conditions that are worse for the dependent self-employed.

This is one of the few papers which provides an extensive evaluation of the working conditions of those in dependent self-employment in the EU28.

Longitudinal regression analysis is conducted to clarify causal relations and control for unwanted influences from actor heterogeneity and state dependence on theoretically important coefficient estimates. Because strategic management contains theory on how firms differ and how firm actions are influenced by their current strategic position and recent experiences, consistency of theory and methodology often requires use of longitudinal methods. We describe the theoretical motivation for longitudinal methods and outline some common methods. Based on a survey of recent articles in strategic management, we argue that longitudinal methods are now used more frequently than before, but the use is still inconsistent and insufficiently justified by theoretical or empirical considerations. In particular, strategic management researchers should use dynamic models more often, and should test for the presence of actor effects, autocorrelation, and heteroscedasticity before applying corrections.

This study examines the value relevance of recognised and disclosed revaluations of land and buildings for a large sample of Australian firms from 1993 through 1997. In contrast to prior research, we control for risk and cyclical effects and find no difference between recognised and disclosed revaluations, using yearly‐cross‐sectional and pooled regressions and using both market and non‐market dependent variables. We also find only weak evidence that revaluations of recognised and disclosed land and buildings are value relevant.

Purpose — A new method of collecting hurricane evacuation data using time-dependent stated choice is developed and evaluated in this study.

Methodology/approach — Hypothetical storms are presented in a video in a sequence of scenarios showing prevailing conditions at discrete points in time as each storm approaches land. Respondents are exposed to nine hypothetical storms representing a range of hurricane characteristics. One of the hypothetical storms is secretly the same as an actual storm the respondents experienced in the past and for which they are required to report their behaviour in a revealed preference survey.

Findings — Stated and actual behaviour was compared and general agreement was found between what people say they would do and what they did. The revealed preference (RP) data was supplemented with time-dependent data from official sources and hurricane evacuation demand models estimated on this enhanced RP data, as well as on a combination of the enhanced RP and time-dependent stated choice (SC) data. When the models were applied to a different data set than the ones on which the models were calibrated, the combined time-dependent RP/SC model performed slightly better than the enhanced RP model. Detailed accounting revealed that time-dependent SC data is 25 percent more expensive to collect than enhanced RP data, although some of this cost may be due to the first-time collection of this type of data.

Artificial neural network (ANN) models, part of the discipline of machine learning and artificial intelligence, are becoming more popular in the marketing literature and in marketing practice. This paper aims to provide a series of tests between ANN models and competing predictive models.

A total of 46 pairs of models were evaluated in an objective model-building environment. Either logistic regression or multiple regression models were developed and then were compared to ANN models using the same set of input variables. Three sets of B2B data were used to test the models. Emphasis also was placed on evaluating small samples.

ANN models tend to generate model predictions that are more accurate or the same as logistic regression models. However, when ANN models are compared to multiple regression models, the results are mixed. For small sample sizes, the modeling results are the same as for larger samples.

Like all marketing research, this application is limited by the methods and the data used to conduct the research. The findings strongly suggest that, because of their predictive accuracy, ANN models will have an important role in the future of B2B marketing research and model-building applications.

ANN models should be carefully considered for potential use in marketing research and model-building applications by B2B academics and practitioners alike.

The research contributes to the B2B marketing literature by providing a more rigorous test on ANN models using B2B data than has been conducted before.

Drawing on self-determination theory, this paper compares the effects of QR code payment method (autonomous vs dependent payment) on payment pleasure, its mechanism and the boundary condition in the mobile payment setting.

Four studies were conducted to examine the effect of QR code payment method on payment pleasure. In study 1, 108 undergraduate students were asked to recall a recent experience when they made either autonomous payment or dependent payment. Study 2 assigned 74 undergraduate students to either the autonomous or dependent payment. Study 3 replicated study 2, but recruited 75 customers in the field. For study 4, a total of 134 undergraduate students participated in a 2 (payment method: autonomous payment vs dependent payment) × 2 (product involvement: high vs low) between-subjects design.

The results of these four studies demonstrate that (1) customers derive more payment pleasure from autonomous payment, compared with dependent payments (study 1); (2) the sense of control mediates the effect of the payment method on payment pleasure (study 2 and study 3); and (3) product involvement moderates the mediating effect of the sense of control (study 4).

These findings contribute to the literature on mobile payment and payment experience. These findings also provide insight to merchants when they select an appropriate payment method and manage the customer payment experience.

This paper aims to identify religiosity scale usage in academic marketing articles and compare the effectiveness of different religiosity scales in predicting marketing and consumer behavior outcomes.

Articles (n = 397) in the top 20 marketing journals are reviewed and a follow-up study is conducted that compares 22 religiosity scales in predicting 18 marketing variables.

Most scales are from preexisting sources (64.3%), only 20% are multi-dimensional and over 58% are used in only one journal article. Only 22.5% of possible regressions in the follow-up study predicting marketing variables from religiosity scales were significant.

This research is limited by the journals and dependent variables chosen. Implications include diversify research topics, expand publication outlets, decrease use of author-generated scales, increase use of multi-item and multi-dimensional measures, replicate findings methodologically and conceptually and make cultural context adaptations.

Marketers would benefit from using preexisting scales, ensuring that religiosity is measured using a multi-item measure that contains appropriate items for the dominant religious beliefs of the sample, as well as consider multi-dimensional measures to best guide marketing strategy decisions, such as target market definition.

This is the first research study to compare the use of religiosity scales in marketing. This offers key value to the marketing literature by highlighting tactics to take to improve consistency in research practices to increase the comparability and accuracy of findings.

The goal is to assess performance prediction systems (PPS) that are used to assist at-risk learners.

The authors propose time-dependent metrics including earliness and stability. The authors investigate the relationships between the various temporal metrics and the precision metrics in order to identify the key earliness points in the prediction process. Authors propose an algorithm for computing earliness. Furthermore, the authors propose using an earliness-stability score (ESS) to investigate the relationship between the earliness of a classifier and its stability. The ESS is used to examine the trade-off between only time-dependent metrics. The aim is to compare its use to the earliness-accuracy score (EAS).

Stability and accuracy are proportional when the system's accuracy increases or decreases over time. However, when the accuracy stagnates or varies slightly, the system's stability is decreasing rather than stagnating. As a result, the use of ESS and EAS is complementary and allows for a better definition of the point of earliness in time by studying the relation-ship between earliness and accuracy on the one hand and earliness and stability on the other.

When evaluating the performance of PPS, the temporal dimension is an important factor that is overlooked by traditional measures current metrics are not well suited to assessing PPS’s ability to predict correctly at the earliest, as well as monitoring predictions stability and evolution over time. Thus, in this work, the authors propose time-dependent metrics, including earliness, stability and the trade-offs, with objective to assess PPS over time.