Wyniki wyszukiwania - BazTech

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Identification of customers’ purchasing behaviour profiles in the context of corporate social responsibility

Kiliańska Katarzyna, Pajęcki Michał

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2022

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z. 162

291--313

EN

Purpose: The aim of the article is to identify customers' purchasing behaviour profiles on the basis of characterizing the process of making a decision to purchase a product from food industry companies’ indicators (observable variables) in the context of corporate social responsibility (CSR). Design/methodology/approach: The data for the research were collected from a survey concerning a group of 801 customers from the Świętokrzyskie Voivodeship. The resources were pre-explored and pre-processed to enable further studies. In order to obtain customers profiles, the latent class analysis (LCA) method was used. It enables identification of homogeneous groups (latent classes) of customers based on selected indicators. Findings: The impact on customers’ purchasing behaviour of 15 CSR activities undertaken by enterprises from several different groups (in relation to: environment, society, employees, contractors, and customers) was examined. Six profiles of customer purchasing behaviour were identified. They were labelled and subjected to descriptive characteristics. Research limitations/implications: The results point out the need to continue the research based on a broader countrywide data set. Practical implications: The research findings can contribute to improving the effectiveness of food industry companies in the range of CSR activities. Due to this, these companies will be able to take more effective steps to retain existing customers and acquire new ones. Social implications: Taking corporate social responsibility actions contributes to solve social and environmental problems. It can also affect the quality of life in a society. Nowadays, it is an important and developmental research area. Originality/value: The conducted study showed that latent class analysis is proper tool for analysing the qualitative data obtained in the questionnaire surveys. The work provides a vital information on the impact of corporate social responsibility activities by food industry companies on customers' purchasing behaviour.

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The optimum strategy for mode choice modelling of interregional fish transport considering shippers' heterogeneity

Ansu V., Anjaneyulu M. V. L. R.

Archives of Transport

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2022

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Vol. 64, iss. 4

7--26

EN

The determinants of mode choice of interregional transport of fish, which is highly perishable, vastly differ from that of other commodities. These determinants are to be identified to improve transport efficiency. A questionnaire survey of shippers is used to collect the data. Highly correlated observed variables are combined to form four latent factors by factor analysis to reduce the errors in modelling. Logical relations among the component variables of latent factors are perceived, and mathematical formulations are used to estimate new variables. It is found that transportation costs and shipment weight contributes to factor 1, while distance contributes to factor 2. However, transportation costs are associated with distance and shipment weight. Thus, the variable, transportation cost per q-km, is estimated. Survey respondents' attitudes are also incorporated into modelling by including qualitative factors obtained by the factor analysis of shippers' preference ratings. A latent class analysis confirmed the existence of heterogeneity among shippers. Misrepresentations of effects occur in modelling if the heterogeneity in the data is not considered. No studies have found the best combination of observed variables, latent factors, estimated variables, and qualitative factors, considering shippers' heterogeneity in freight mode choice. Hence, this study is done to find the optimum modelling strategy. Modelling revealed that models built with estimated variables outperformed those built with latent factors. Including qualitative factors along with observed variables and estimated variables showed further improvement. However, the model that includes observed variables, estimated variables, and qualitative factors considering shippers' heterogeneity is the best. It was found that the mode selection behaviour of different latent classes of shippers is distinct. A mode shift from road to rail could be achieved by lowering transportation costs and increasing speed, reliability, and safety for fish transport. Expanding roll on-roll off facilities, dedicated freight corridors, parcel trains, refrigerated containers, and piecemeal service by rail promote a mode shift from road to rail and reduce energy usage.

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Rozkład a priori w czynniku bayesowskim a wybór modelu klas ukrytych

Kapłon R.

Badania Operacyjne i Decyzje

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2009

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nr 3

88-94

PL

Na etapie wyboru liczby segmentów w analizie klas ukrytych kryteria informacyjne są często stosowane. Szczególne miejsce zajmuje tutaj kryterium bayesowskie BIC, które można wyprowadzić – dokonując pewnych uproszczeń – z koncepcji czynnika bayesowskiego. W czynniku tym pojawia się rozkład a priori parametrów, którego nie ma w BIC. Z tego względu w pracy podjęto próbę znalezienia takiego rozkładu a priori, aby skuteczność tak powstałego kryterium była większa niż skuteczność BIC.

EN

Estimating the values of parameters in latent class analysis, one needs to know the number of clusters in advance. It is crucial to determine a criterion which enables confirmation of the superiority of one number of classes over the others. A statistical approach, which is based on a likelihood ratio test (LRT), contends with the difficulties of assessing the null distribution of LRT statistics. As a remedy, information criteria like the Bayesian information criterion (BIC) can be used. This criterion is an approximation of a Bayes factor that depends on the prior distribution. Apparently, if one combines BIC and a suitable prior, the effectiveness of such a criterion increases in comparison to the standard BIC. In this article we propose such a prior distribution. In order to do this, a simulation study is carried out and the data collected enable the construction of a nonlinear regression model. The number of classes and the values of the required parameter are chosen as the predictor and the dependent variable, respectively. Such an approach enables the estimation of the values of the parameters a priori given the number of clusters. The performance of the new criterion is better than the Bayesian information criterion by up to 58%.