This article aims to develop the zero-inflated negative binomial-Lindley regression model to address the complexity of count data with zero excess and over-dispersion. The proposed compound distribution combines the zero generation mechanism with the Lindley distribution process, and the Bayesian hierarchical framework with MCMC sampling is adopted for parameter estimation, overcoming the limitations of traditional count models in handling complex data structures. The model is applied to two real datasets, one of which is characterized by a large number of zero observations. Its performance is compared with that of the NB-L and NB model. The results show that when the dataset presents the large number of zero values and the long tail feature, the ZINB-L GLM describes the dataset better than the other models.

The idea of supply chain management (SCM) is covering its perspective in the form of flow of material and information from the business to customers. Its application in the field of tourism is also very much significant. The objective of present study is to analyze the empirical association between the supply chain management and tourism industry from the context of hotel industry in Indonesia. For this purpose, a questionnaire-based approach is followed while taking the demographic factors regarding age, gender and qualification. A sample of 272 respondents is finally accepted for the empirical examination between supply chain and its implication in tourism industry. Both descriptive and regression analysis are conducted. To analyze the factor of supply chain, sixteen items are considered from the existing literature while considering the strategic supplier partnership, information sharing, and information quality as the key indicators. The factor of tourism is meas-ured through four different proxies. The practical implications of the study are taken both the tourists and key policymakers while dealing with the SCM and its integration with those hotels dealing with the tourism and related services. This study contributes towards the future trends in the form of integration for SCM and tourism industry.

Branded fashion clothing is developing expeditiously in Bangladesh. Bangladeshi local apparel fashion brand outlets are flourishing with positive customers’ attitude. The aim of this research is to understand the effect of brand equity on customers’ desire to spend on a premium price on a shop-ping store named Aarong. The research tries to discover different aspects of brand equity by analyz-ing the customers’ manner and buying behavior. In this research, Aaker’s brand equity model is used to examine the influence of brand equity on consumers’ intention to pay for a premium cost. The necessary data for the study is collected from 100 respondents using the non-probability judgmental sampling technique. The collected data is analyzed by regression analysis so that the relationship can be examined, properly and the results support the proposed four hypotheses. It is found that among four brand equity variables, brand association maintained the most significant impact on creating positive customer intention to pay the premium price. Other variables such as brand recognition, discerned quality and brand faithfulness only slightly create positive influence on customer intention to pay the premium price. Thus, the results indicate that to ensure good performance and attract customers, Aarong must put more emphasis on improving all brand equity variables and maintain a suitable balance among them.

This research aims to explore the impact of AI-induced anxiety on the adoption of mobile payment services in supply chain firms, viewed through the lens of Mental Accounting Theory. In an era driven by technological advancement, supply chain companies' use of mobile payment services has arisen as a crucial problem. This study is the first to investigate the complicated links between AI-induced anxiety, perceived utility, and the adoption rate of mobile payment systems using the Mental Accounting Theory as a theoretical framework. The study employs a quantitative research approach, using Smart PLS for regression analysis, and gathers its data from major supply chain business players. Our analysis offered important insights into the many aspects influencing the adoption of mobile payment services in supply chain companies. The acceptance rate was shown to be adversely connected with AI-induced anxiety and integration expenses, posing obstacles for businesses seeking to embrace mobile payment systems. In contrast, characteristics such as perceived utility, usability, confidence in security, and backing from upper management were positively connected with adoption rates. These findings provide not only theoretical contributions to the current research, but also concrete advice for supply chain practitioners seeking to exploit mobile payment systems for operational and strategic advantage.

Green banking encourages sustainable economic development and stimulates environmentally friendly activities. The objective of this study is to assess determinants of green banking adoption in Vietnam. Data of the study was collected through a survey of officials and employees at commercial banks in Vietnam. The results of correlation and regression analysis indicate that the influence of macro factors on the level of green banking is the strongest, followed by the bank's financial capacity, government's supportive policies and green investment needs of business organizations. Conversely, the capacity of officials and the bank leader awareness on green banking do not significantly affect the level of green banking adoption in Vietnam.

Two sets of nine different silicon carbide particulate (SiCp) reinforced Al 7075 Metal Matrix Composites (MMCs) were fabricated using liquid metallurgy stir casting process. Mean particle size and weight percentage of the reinforcement were varied according to Taguchi L9 Design of Experiments (DOE). One set of the cast composites were then heat treated to T6 condition. Optical micrographs of the MMCs reveal consistent dispersion of reinforcements in the matrix phase. Mechanical properties were determined for both as-cast and heat treated MMCs for comparison of the experimental results. Linear regression models were developed for mechanical properties of the heat treated MMCs using list square method of regression analysis. The fabrication process parameters were then optimized using Taguchi based grey relational analysis for the multiple mechanical properties of the heat treated MMCs. The largest value of mean grey relational grade was obtained for the composite with mean particle size 6.18 µm and 25 weight % of reinforcement. The optimal combination of process parameters were then verified through confirmation experiments, which resulted 42% of improvement in the grey relational grade. Finally, the percentage of contribution of each process parameter on the multiple performance characteristics was calculated through Analysis of Variance (ANOVA).

Powder mixed electro discharge machining (PMEDM) is a hybrid machining process where electrically conductive powder is suspended into a dielectric medium, for enhancing the material removal as well as the surface finish. In this investigation, electro discharge machining (EDM) has been performed for the machining of AISI 304 stainless steel by using the tungsten carbide electrode, when silicon carbide (SiC) powder is suspended into kerosene dielectric medium. Peak current, pulse on time, gap voltage, duty cycle and powder concentration are considered as process parameter while the surface roughness (Ra) is the only response. The effect of significant process parameters on the response has been studied. A regression analysis has been performed to describe the correlation of data between the machining parameter, and the response. Microstructural analysis has been done for the PMEDMed surface. The result shows that peak current is the most influential parameter for surface roughness. Surface roughness decreases with the increase of powder concentration.

Knowledge management plays an essential role on developing efficient systems in educational systems. However, there are different factors influencing the success of knowledge management. In this paper, we present an empirical study to measure the impact of six cultural based factors including management support, organizational affiliation, employee participation in decision-making, staff welfare organization and establishment, adaptation of new policies and organizational and internal organizational climate on establishment of knowledge management. The proposed study of this paper is implemented in 114 selected educational organizations in city of Tehran, Iran. A questionnaire is designed in Likert scale, it is distributed among experts, and using regressions analysis and structural equation modeling, we have analyzed the data. The results of regression analysis indicate that management support, staff welfare organization and internal organizational climate are among the most important factors while other component did not represent any significance on knowledge management implementation.

E-learning has recently attracted many people specially those who cannot attend traditional educational programs for different reasons such as busy schedule, long distance, etc. There is also a growing interest among many public universities to develop such programs in addition to their regular business activities. The primary question is to know how much a program cost and what is the most important factors affecting the cost of e-learning. In this paper, we present an empirical study the measure the effects of different items on the cost of e-learning programs. The proposed study of this paper gathers the necessary cost components of three governmental universities located in city of Tehran/Iran. We choose ten different cost items for our analysis and using some statistical test determine the most important ones. The preliminary study of this paper indicates that seven cost items play essential role on determining the cost of e-learning, which includes utilities, internet connection and network, employee payment, software & licensing, payments of instructors and their assistants, hardware equipments and other overhead expenditures. This could help us determine the precise cost of e-learning using activity cost method.

Sugar is one of the strategic goods in the basket of households in each country and it plays an
important role in supplying the required energy. On the other hand, it is one of the goods, which
Iranian government is about to change its subsidy strategies. To design useful sugar subsidy
strategies, it is necessary to know sugar position in the basket of households and be familiar
with households & apos; sugar demand or consumption behavior. This research estimates sugar demand
for Iranian households by using time series of 1984-2008, which is taken from central bank of
Iran. In this paper, first independent and dependent variables of household sugar demand
model are chosen based on the literature review and theory of demand. Then, sugar demand is
estimated by OLS technique and linear regression. The preliminary statistical observations such
as Durbin-Watson, F statistic and R2 indicate that the regression is admissible. The results seem
plausible and consistent with theory and show that sugar demand in Iranian households is
associated with household expenditure, relative sugar price, family size and indicate that
demand of sugar is affected during the war time. The results also show the income elasticity is
0.8 and price elasticity is -0.2 which means sugar is essential good for Iranian households and is
inelastic to price.