Influence of personality traits on Facebook engagement and their effects on socialization behavior and satisfaction with university life

Several factors influence Facebook users’ engagement. This study investigates the correlations between Facebook engagement and users’ personality traits. This study also analyses the impact of Facebook engagement on students’ socialization behavior and their satisfaction with their university life. A dataset of 994 Malaysian students was collected and used for analysis. The outcome of the analysis illustrates that students’ personality traits have a positive influence on Facebook engagement. Subsequently, Facebook engagement was found to have an influence over students’ socialization behavior and their satisfaction with university life. The results can be used by educators as a guide for their curriculum development, i.e., consideration of wider usage of Facebook in conducting courses.     

A doubly-inflated Poisson regression for correlated count data

Count data have emerged in many applied research areas. In recent years, there has been a considerable interest in models for count data. In modelling such data, it is common to face a large frequency of zeroes. The data are regarded as zero-inflated when the frequency of observed zeroes is larger than what is expected from a theoretical distribution such as Poisson distribution, as a standard model for analysing count data. Data analysis, using the simple Poisson model, may lead to over-dispersion. Several classes of different mixture models were proposed for handling zero-inflated data. But they do not apply to cases when inflated counts happen at some other points, in addition to zero. In these cases, a doubly-inflated Poisson model has been suggested which only be used for cross-sectional data and cannot consider correlations between observations. However, correlated count data have a large application, especially in the health and medical fields. The present study aims to introduce a Doubly-Inflated Poisson models with random effect for correlated doubly-inflated data. Then, the best performance of the proposed method is shown via different simulation scenarios. Finally, the proposed model is applied to a dental study.KEYWORDS: Count data, doubly-inflated, Poisson regression, zero-inflated, correlated data     

NERJIT: Using Net Requirement Data in Kanban‐Controlled Jumbled‐Flow Shops

The bold lines that have separated the application of specific production planning and control techniques to specific production systems are being blurred by continuous advances in production technologies and innovative operational procedures. Oral communication among dispatchers and production units has given way to electronic communication between production planners and these units by continuous progress in information technologies. Current production literature alludes to the idea that, collectively, these advances have paved the way for application of Just‐In‐Time (JIT) production concepts, which were originally developed for mass production systems, in intermittent production systems. But this literature does not actually consider the possibility. This article presents a modification to JIT procedures to make them more suitable for jumbled‐flow shops. This article suggests providing real‐time information about net‐requirements for each product to each work center operator for setting production priorities at each work center. Simulation experiments conducted for this study show that using Net‐Requirements in JIT (NERJIT) reduces customer wait time by 45–60% while reducing inventory slightly. The analysis of work centers’ input and output stock‐point inventories shows that using the information about net‐requirements results in production of items that are in current demand. NERJIT results in smaller input stock‐point inventory and availability of products with higher priority in the output stock‐points of work centers.     

Divide and rule: The effects of diversity and network structure on a firm's sustainability performance

To tackle sustainability, firms often use partnerships with organizations from different industries or societal sectors such as government and civil society. While partnerships show potential for firms to improve their sustainability performance, they not only lead to a potential to learn from diversity but also to tensions due to a lack of unity between partners. In partnerships for sustainability, this unity-diversity tension particularly manifests itself in different views of sustainability. This paper examines how this tension affects the sustainability performance of firms taking part in partnerships for sustainability in developing countries. To address this question, this tension is conceptualized as being the result of differences in organizational frames and institutional logics between a focal firm and other organizations in the partnership. It is also taken into consideration that firms are embedded in an overarching, relational network structure that allows them to manage the diversity in frames and logics. To test the hypotheses, the study analyses 1353 greenhouse gas emission reduction projects of 322 firms from developing countries active in the carbon-offset market from 2007 to 2009. The findings show that if partners hold different frames and belong to different sectors, a firm's sustainability performance improves first due to learning from diversity but after a turning point decreases from a lack of unity. This inverse U-shape relationship is flipped if a firm occupies a brokerage position in its network.     

A change point method for Phase II monitoring of generalized linear profiles

In this article, we adopt the change point approach to monitor the generalized linear profiles in phase II Statistical process control (SPC). Generalized linear profiles include a large class of profiles defined in one framework. In contrast to the conventional change point approach, we adopt the Rao score test rather than the likelihood ratio test. Simulated results show that our approach has a good performance over any possible single step change in process parameters for two special cases of generalized linear profiles, namely Poisson and binomial profiles. Some diagnostic aids are also given and a real example is introduced to shed light on the merits of our approach in real applications.     

Hotelling’s T2 control chart with double warning lines

Recent studies have shown that the T ² control chart with variable sampling intervals (VSI) and/or variable sample sizes (VSS) detects process shifts faster than the traditional T ² chart. This article extends these studies for processes that are monitored with VSI and VSS using double warning lines (T ² —DWL). It is assumed that the length of time the process remains in control has exponential distribution. The properties of T ² —DWL chart are obtained using Markov chains. The results show that the T ² —DWL chart is quicker than VSI and/or VSS charts in detecting almost all shifts in the process mean.     

An Analysis of Dual‐Kanban Just‐In‐Time Systems in a Non‐Repetitive Environment

Recent advances in approaches and production technologies for the production of goods and services have made just‐in‐time (JIT) a strong alternative for use in intermittent and small batch production systems, especially when time‐based competition is the norm and a low inventory is a must. However, the conventional JIT system is designed for mass production with a stable master production schedule. This paper suggests supplementing the information provided by production kanbans with information about customer waiting lines to be used by operators to schedule production in each work‐station of intermittent and small batch production systems. This paper uses simulation to analyze the effect of four scheduling policy variables—number of kanbans, length of the withdrawal cycle, information about customer waiting lines, and priority rules on two performance measures—customer wait‐time and inventory. The results show that using information about customer waiting lines reduces customer wait‐time by about 30% while also reducing inventory by about 2%. In addition, the effect of information about customer waiting lines overshadows the effect of priority rules on customer wait‐time and inventory.