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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A production company size and workplace safety hazards

Nowakowska Marzena, Pajęcki Michał

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2022

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z. 162

527--542

EN

Purpose: The objective of the study is to examine whether a wood processing company size affects the differentiation of workplace safety hazards as well as to investigate the influence of features characterizing an occupational accident casualty on their injury severity, considering the company size. Methodology: The study used non-aggregated data obtained from the Central Statistical Office, Poland. The data for analyzes were prepared through quality diagnosis, cleaning and transformation. Variables of no informative value were excluded from further investigation. Statistical tests were performed implicating the need for independent analyzes for two data subsets referring to: micro and small enterprises (employing up to 49 persons), and medium and large enterprises (employing 50 persons or more). For each of the two company groups, a logistic model was developed classifying the occupational accident casualty injury severity based on the casualty characteristics. In each case, the classification quality was assessed using a test data set. Findings: It was shown that the enterprise size had an impact on the severity of accidents at work and that the proposed method of classifying enterprises by size into two categories was justified. Explanatory variables in logistic models were interpreted according to their importance and intensity of influence on the explained variable. Practical implications: The obtained results can be used in the development of materials on occupational safety risks for entrepreneurs and OSH services. Social implications: Each type of economic activity carries various risks. Occupational accidents pose a serious social and economic problem. Research in the field of occupational safety allows a better understanding of the nature of such accidents and makes it possible to take effective preventive actions which, however, can depend on a company size. Originality/value: On the basis of the obtained results, it is possible to identify the factors influencing the severity of occupational accidents in wood processing companies according to their size. The research also showed that bivariate multiple logistic regression is an appropriate tool for analyzing occupational accident data.

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Applying latent class analysis in the identification of occupational accident patterns

Nowakowska Marzena, Pajęcki Michał

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2020

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z. 146

339--355

EN

Purpose: The objective of the study is to use selected data mining techniques to discover patterns of certain recurring mechanisms related to the occurrence of occupational accidents in relation to production processes. Design/methodology/approach: The latent class analysis (LCA) method was employed in the investigation. This statistical modeling technique enables discovering mutually exclusive homogenous classes of objects in a multivariate data set on the basis of observable qualitative variables, defining the class homogeneity in terms of probabilities. Due to a bilateral agreement, Statistics Poland provided individual record-level real data for the research. Then the data were preprocessed to enable the LCA model identification. Pilot studies were conducted in relation to occupational accidents registered in production plants in 2008-2017 in the Wielkopolskie voivodeship. Findings: Three severe accident patterns and two light accident patterns represented by latent classes were obtained. The classes were subjected to descriptive characteristics and labeling, using interpretable results presented in the form of probabilities classifying categories of observable variables, symptomatic for a given latent class. Research limitations/implications: The results from the pilot studies indicate the necessity to continue the research based on a larger data set along with the analysis development, particularly as regards selecting indicators for the latent class model characterization. Practical implications: The identification of occupational accident patterns related to the production process can play a vital role in the elaboration of efficient safety countermeasures that can help to improve the prevention and outcome mitigation of such accidents among workers. Social implications: Creating a safe work environment comprises the quality of life of workers, their families, thus affirming the enterprises' principles and values in the area of corporate social responsibility. Originality/value: The investigation showed that latent class analysis is a promising tool supporting the scientific research in discovering the patterns of occupational accidents. The proposed investigation approach indicates the importance for the research both in terms of the availability of non-aggregated occupational accident data as well as the type of value aggregation of the variables taken for the analysis.