Contagious diseases and tourism: a systematic review based on bibliometric and content analysis methods

Quality & Quantity - The present study examines the existing knowledge and intellectual structure on contagious diseases and tourism to map the development of the concept through collaborative...     

A multivariate Poisson mixture model for marketing applications

This paper describes a multivariate Poisson mixture model for clustering supermarket shoppers based on their purchase frequency in a set of product categories. The multivariate nature of the model accounts for cross-selling effects between the purchases made in different product categories. However, for computational reasons, most multivariate approaches limit the covariance structure by including just one common interaction term, or by not including any covariance at all. Although this reduces the number of parameters significantly, it is often too simplistic as typically multiple interactions exist on different levels. This paper proposes a theoretically more complete variance/covariance structure of the multivariate Poisson model, based on domain knowledge or preliminary statistical analysis of significant purchase interaction effects in the data. Consequently, the model does not contain more parameters than necessary, whilst still accounting for the existing covariance in the data. Practically, retail category managers can use the model to devise customized merchandising strategies.     

Outcomes of tumor necrosis factor inhibitor cycling versus switching to a disease-modifying anti-rheumatic drug with a new mechanism of action among patients with rheumatoid arthritis

Objectives: To examine treatment patterns, treatment effectiveness, and treatment costs for 1 year after patients with rheumatoid arthritis switched from a tumor necrosis factor inhibitor (TNFi) (adalimumab, certolizumab pegol, etanercept, golimumab, or infliximab), either cycling to another TNFi (“TNFi cyclers”) or switching to a new mechanism of action (abatacept, tocilizumab, or tofacitinib) (“new MOA switchers”).

Methods: This retrospective cohort study used administrative claims data for a national insurer. Treatment persistence (without switching again, restarting, or discontinuing), treatment effectiveness (defined below), and costs were assessed for the 12-month post-switch period. Patients were “effectively treated” if they satisfied all six criteria for a treatment effectiveness algorithm (high adherence, no dose increase, no new conventional synthetic disease-modifying anti-rheumatic drug, no subsequent switch in therapy, no new/increased oral glucocorticoids, and <2 glucocorticoid injections). Multivariable logistic models were used to adjust for baseline factors.

Results: The database included 581 new MOA switchers and 935 TNFi cyclers. New MOA switchers were 39% more likely than TNFi cyclers to persist after the switch (odds ratio [OR]?=?1.39; 95% confidence interval [CI]?=?1.12–1.74; p?=?.003) and 36% less likely to switch therapy again (OR?=?0.64; 95% CI?=?0.51–0.81; p?p?=?.006). New MOA switchers had 16% lower drug costs than TNFi cyclers (cost ratio?=?0.84; 95% CI?=?0.79–0.88; p?p?Limitations: Claims payments may not reflect rebates or other cost offsets. Medical and pharmacy claims do not include clinical end-points or reasons that lead to new MOA switching vs TNFi cycling.

Conclusions: These results support switching to a new MOA after a patient fails treatment with a TNFi, which is consistent with recent guidelines for the pharmacologic management of established rheumatoid arthritis.     

Modeling social interactions: Identification, empirical methods and policy implications

Social interactions occur when agents in a network affect other agents’ choices directly, as opposed to via the intermediation of markets. The study of such interactions and the resultant outcomes has long been an area of interest across a wide variety of social sciences. With the advent of electronic media that facilitate and record such interactions, this interest has grown sharply in the business world as well. In this paper, we provide a brief summary of what is known so far, discuss the main challenges for researchers interested in this area, and provide a common vocabulary that will hopefully engender future (cross disciplinary) research. The paper considers the challenges of distinguishing actual causal social interactions from other phenomena that may lead to a false inference of causality. Further, we distinguish between two broadly defined types of social interactions that relate to how strongly interactions spread through a network. We also provide a very selective review of how insights from other disciplines can improve and inform modeling choices. Finally, we discuss how models of social interaction can be used to provide guidelines for marketing policy and conclude with thoughts on future research directions.     

Scale Development and Validation for Measuring Online Engagement

Although the construct of online engagement has received considerable attention in the consumer behavior literature, academic research lacks clear conceptualization and rigorous measurement of the construct within social media. Further, a lack of consensus remains with regard to the conceptualization and operationalization of engagement in the marketing literature. This study develops and validates a 16-item online engagement scale comprising four factors: conscious attention, affection, enthused participation, and social connection. Strong evidence supports the reliability of the multidimensional conceptualization, as well as its convergent and discriminant validity. The scale offers a framework for future research investigations in an increasingly important area.     

Size Does Matter: Technical and Scale Efficiency in Indian State Tax Jurisdictions

Data Envelopment Analysis (DEA) is used to measure tax efficiency in 15Indian states from 1980/81 to 1992/93. Tax efficiency is shown to be conditional on state gross domestic product (SDP), agriculture's share in state SDP,and a poverty index. The considerable remaining efficiency differences areattributable to the small size of some tax jurisdiction rather than to technical inefficiency. Multilateral Malmquist tax indices show that six of the states were consistently efficient, while three were consistently inefficient. Tax efficiency grew at an average annual rate of 3.9% until 1986/87, but growth ceased after that date for all but two states.     

An economic analysis of leasebacks

Leaseback decisions by firms involve the simultaneous sale of an asset and a lease agreement with the new owner. Examination of 64 leaseback decisions made by firms during 1979–1987 reveals a significant negative announcement effect. We present a theoretical framework in which all firms prefer to leaseback when there is symmetric information. When there is asymmetry of information between the manager and the market, however, firms with favorable prospects prefer to own the asset. Firms with poor prospects choose to leaseback and capture the associated depreciation tax shield through the sale. Our empirical results indicate that, besides the significant negative announcement effect, firms proposing a leaseback earn negative returns in the three months prior to the announcement. We monitor the performance of these firms for five years after the leaseback decision and compare it to five years before the announcement. There is a significant drop in operating performance as indicated by several key variables such as operating earnings before depreciation and pretax earnings. This is consistent with the hypothesis that firms choose to leaseback when faced with unfavorable future prospects. I would like to thank S.P. Kothari, Cheng-few Lee, Scott Linn, Mike Rozeff, Ramasastry Ambarisha, and an anonymous referee for their comments and suggestions. The usual disclaimer applies.     

Analysing the impact of restructuring transportation,production and distribution on costs and environment – a case from the Thai Rubber industry

Transportation and distribution are key elements to successful supply chains, however there is some disagreement regarding the impact of distribution and transportation restructuring on costs and the environment. This paper explores the use of an optimisation model of Thailand’s rubber industry supply chain, to assess the impact of distribution and transportation on costs and greenhouse gas emissions. It has previously been observed that there is a positive correlation between transportation cost reduction and environmental impact, nevertheless the correlation is not clearly established when the distribution system is restructured. This paper is divided into two parts: the first part examines the impact of transportation service capacity on distribution decisions; the second part of the paper aims to examine the impact of restructuring the distribution network considering multi-modal options on cost and greenhouse gas emissions. For both parts a scenario analysis is utilised in conjunction with an optimisation model to derive the best possible answer in terms of costs and GHG emissions. In this paper, the results obtained indicate that the impact on cost minimisation from the increase in rail freight service capacity is marginal, while the impact on GHG emission minimisation is more significant. In terms of short-sea shipping prices and service capacity, the scenario analysis shows a slight positive impact on cost minimisation but no positive or negative impact on GHG emission minimisation. Results also confirm that in terms of economic advantages, distribution network restructuring provides greater benefit to the industry than does capacity development for the transportation service.     

A new multivariate count data model to study multi-category physician prescription behavior

Multivariate count models represent a natural way of accommodating data from multiple product categories when the dependent variable in each category is represented by a positive integer. In this paper, we propose a new simultaneous equation multi-category count data model–the Poisson-lognormal simultaneous equation model–that allows for the Poisson parameter in one equation to be a function of the Poisson parameters in other equations. While generally applicable to any situation where simultaneity is an issue and the dependent variables are measured as counts, such a specification is particularly useful for our empirical application where physicians prescribe drugs in multiple categories. Accounting for the endogeneity of detailing in such situations requires us to explicitly allow for pharmaceutical firms’ detailing activities in one category to be influenced by their activities in other categories. Estimation of such a system of equations using traditional maximum likelihood method is cumbersome, so we propose a simple solution based on using Markov Chain Monte Carlo methods. Our simulation study demonstrates the validity of the solution algorithm and the biases that would result if such simultaneity is ignored in the estimation process.     

Analyzing Customers' Switching Intentions in the Telecom Sector

ABSTRACT

Mobile Number Portability (MNP) has not only affected customers' behavioral patterns, but also increased competition among telecom operators. Against this backdrop, the study examines the relationship between customers' switching intentions and attitude towards MNP, customer loyalty, and switching barriers. It also investigates the impact of these intentions on actual switching or staying behavior of customers. Data were collected from a sample of 260 telecom customers comprising university students. Findings reveal that customers' switching intentions would largely depend upon switching barriers perceived by them. Switching intentions predict both actual staying and switching behavior. Our understanding of switching barriers is limited to those involved in switching from a current service provider to a new service provider. Future research can examine the barriers associated with returning to the original provider and the effect on the decision to leave in the first place, especially in the case of contractual agreements such as BSNL. The present study will help practitioners and telecom service providers in understanding the factors affecting customers' switching intentions to prevent switching.