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This article presents an analysis of the participation of Texas public school districts in the Advanced Placement (AP) program for academic years ending 2013 through 2019. We develop and estimate to a panel of school district data a hurdle count model composed of (1) a first stage fixed effect binomial logistic regression model of the probability that a school district opts-in to locally offer AP program courses in a given academic years and (2) a second stage fixed effect zero truncated Poisson regression model of the number of unique AP courses offered in a given year by opting-in districts. We find that the number of Texas school districts providing local access to AP courses is decreasing through time; however, we also find that districts that do provide local access to AP courses offer increasingly greater numbers of unique AP courses.

Purpose This study aims to understand how informational factors influence online purchase intention when considering secondhand books. Design/methodology/approach A conceptual model linking book condition, description, delivery cost, picture, sellers’ rating and delivery date to purchase intention was developed and tested by using structural equation modeling. Survey data from 234 respondents was used to analyze both direct and mediating relationships. Findings The examination demonstrates how book condition, delivery cost and sellers’ rating influence consumers’ purchase intention. Book condition directly and indirectly influenced purchase intention, while delivery cost and sellers’ rating were significant only when including the mediating variable delivery date. Originality/value This work clarifies where resources should be allocated when offering secondhand books online. Sellers should dedicate time to include informational factors such as book condition, delivery cost and sellers’ rating. Contra wise, there is little value expounding on the book’s description or providing a high-quality picture when selling online.

In this paper we propose and test a staged knapsack model to create a supplier portfolio for retailers. The first two stages in the model use judgement criteria related to supplier characteristics ...

PurposeThe purpose of this paper is to understand how task conflict and relationship conflict influence teams’ transactive memory systems (TMS) and by extension team performance.Design/methodology/approachLeveraging experiential learning theory and a popular operations management simulation tool, survey data from 341 students, who worked on 117 simulation teams, are collected. To examine the present hypotheses bootstrapping analysis and SPSS were used.FindingsBoth task and relationship conflict can significantly diminish TMS development, which in turn, inhibits team performance. Thus, when teams disagree on how to approach a task, conflict could diminish TMS formation. In addition, when one team member has a personal conflict with one or more members that it further amplifies the influence of task conflict. To address the negative influence of both task and relationship conflict, teams should develop processes to better utilize members’ specialized knowledge and work together in a coordinated manner.Research limitations/implicationsThe research adds to the literature by articulating the mediating influence that relationship conflict has on task conflict. Furthermore, it highlights how teams can develop TMS as a means to improve team performance when using simulation tools as a teaching device.Originality/valueThis work broadens our understanding of the conditions under which educators can teach students about teams and teamwork capabilities. In addition, the authors expand the use of simulations as an experiential learning tool.

This article presents an exploratory case study examining the effects of different early college credit programs on time to baccalaureate degree attainment at a metropolitan Midwestern United States land grant university. We developed a Cox proportional hazards regression model of a students’ time to degree attainment as a function of their participation in different early college credit programs while controlling for each student’s preenrollment grade point average (GPA), American College Test (ACT) test score, gender, part or full-time enrollment status, ethnicity, and seasonal (Fall, Spring, or Summer) semester of initial matriculation. The most noteworthy finding of our analysis was that each early college credit program appeared to have a positive and statistically significant impact on reducing the time to degree attainment with all other factors being equal. However, the mechanisms through which these programs affect the time to degree attainment appeared to be differentiated by program.

Leveraging a popular operations management simulation, we examine how team interaction, students' previous software experience, simulations' ease of use, team leadership, and instructors' guidance affects students' comprehension. Respondents completed a 27-item survey designed to assess individual comprehension. To illustrate the various affects, we use structural equation modeling to compare first-generation and continuing-generation undergraduate populations. The results indicate software's ease of use and instructors' guidance affects both groups, while team interaction to comprehension is present for only first-generation students. We also find that the strength of the various relationships depends on the population of students being studied. As simulations continue to grow in popularity, educators need to recognize how these pedagogical tools affect different student populations.

Purpose This study aims to understand how mode of delivery, online versus face-to-face, affects comprehension when teaching operations management concepts via a simulation. Conceptually, the aim is to identify factors that influence the students’ ability to learn and retain new concepts. Design/methodology/approach Leveraging Littlefield Technologies’ simulation, the study investigates how team interaction, team leadership, instructor’s guidance, simulation’s ease of use and previous software experience affects comprehension for both online and face-to-face teaching environments. Survey data were gathered from 514 undergraduate students. The data were then analyzed using structural equation modeling. Findings For the face-to-face population, this study found that team interaction, previous software experience, instructor’s guidance and simulation’s ease of use affected student comprehension. This differed from the online population who were only affected by the simulation’s ease of use and instructor’s guidance. Originality/value Understanding how the mode of delivery affects comprehension is important as educators develop new online teaching techniques and experiment with innovative technologies like simulation. As demand for online education grows, many instructors find they need to refine their methods to ensure students comprehend the concepts being taught regardless of modality.

In this study, we investigate the joint impacts of class size, modality of content delivery and instructor workloads on 1078 student performances from 2010 through 2014 in a required College of Bus...

The school bus routing problem (SBRP) is a challenging operations research problem that has been studied by researchers for almost 50 years. SBRP publications address one or more operational sub-problems, including: bus stop selection, bus route generation, bus route scheduling, school bell time adjustment, and strategic transportation policy issues. This paper reviews 64 new SBRP research publications and analyzes them by sub-problem type, problem characteristics and solution approach. The impact of key SBRP characteristics (number of schools, mixed load, fleet mix, service environment, objective and constraints) are discussed and the different solution approaches to the SBRP are summarized by sub-problem type and methodology. We found in recent years, SBRP researchers are examining more complex real-world problem settings, adopting both evolutionary-based and trajectory-based metaheuristic solution approaches, and considering ridership and travel time uncertainty. This review documents recent trends in SBRP research and highlights research gaps and promising opportunities for future SBRP research.

School bus routing is a complex and expensive transportation problem for many public school districts. Typical school bus routes serve a single school, but mixed load school bus routes carry students for more than one school at the same time. A mixed load policy reduces the number of stops and distance to pick up and drop off children, but it can increase travel distance by visiting multiple schools. This paper provides a general strategic analysis using continuous approximation models to assess the conditions under which mixed loading is likely to be beneficial. We also present a case study for a semi-rural Missouri school district to illustrate the application of the models in practice. Results show that mixed load routing is more beneficial for larger districts, when a large percentage of bus stops are shared by students of different schools, and when schools are closer together.

Transporting pupils to and from schools is a complex and expensive logistics problem for many public school districts, especially in rural areas where travel distances are longer. In many regions of the world, students ride public transit to school, but public school districts in the US and Canada generally provide transportation in dedicated school buses. Each bus typically makes a sequence of trips each morning and each afternoon, where each trip serves a separate school, usually with staggered start times for different school levels (elementary school, intermediate school, high school). This research explores whether the successful business logistics practice of mixed loading can be applied to school bus transportation. Mixed load school bus trips carry students for more than one school at the same time, and a mixed load routing policy reduces the number of stops to pick up and drop off students, but it adds travel distance at the end of a trip to visit multiple schools. We first provide a general strategic analysis using continuous approximation modeling to assess the conditions under which mixed loading is likely to be beneficial. Then we present a discrete algorithm for finding mixed load bus trips. Results for benchmark data sets explore the tradeoffs between minimizing the number of buses used and minimizing the travel distance. We also present a case study for a Missouri school district to illustrate the application of the models in practice. Results show that mixed load bus routing can be beneficial when students are sparsely distributed, when a large percentage of bus stops are shared by students of different schools, and when schools are closer together.