Factors influencing customer engagement with branded content in the social network sites of integrated resorts

The integrated resorts rely on social network sites (SNSs) for content marketing, while one of the main purposes of content marketing is customer engagement that will leverage the impact of branded content. This study investigates the factors influencing customer engagement with brand posts of integrated resorts on the SNSs. The findings highlight the important influence of both media-type and content-type factors on the level of customer engagement in SNSs of integrated resorts. This study provides practical implications for integrated resorts to develop SNS online content marketing strategy.     

Broadband Demand Analysis for Network Building

An increase of broadband demand is forecasted by transitional methods that consider the effect of this increase through many factors, such as customer requirement diversification, and new service introduction and deployment under competition. Broadband demand forecasting has become important for closing the digital divide, promoting regional developments, and constructing networks economically; therefore, a demand forecast model that considers the mechanisms of market structure is necessary. In this paper, a demand analysis method for broadband access combining macro- and micro-data mining is proposed, and the service choice behaviour of customers is introduced as a customer model not only to express the macro trend of market structure, but also to consider area marketing. The proposed method can estimate the potential demand, determine the point at which broadband demand growth peaks in a specified area, and support a decision for ultra high-speed broadband access facility installation.     

Impact of financial/economic crisis on demand for hotel rooms in Hong Kong

The main objectives of this study are (1) to identify the factors that influence the demand for hotel rooms in Hong Kong and (2) to generate quarterly forecasts of that demand to assess the impact of the ongoing financial/economic crisis. The demand for four types of hotel room from the residents of nine major origin countries is considered, and forecasts are generated from the first quarter of 2009 to the fourth quarter of 2015. Econometric approaches are employed to calculate the demand elasticities and their corresponding confidence intervals, which are then used to generate interval demand predictions. The empirical results reveal that the most important factors in determining the demand for hotel rooms in Hong Kong are the economic conditions (measured by income level) in the origin markets, the price of the hotel rooms and the ‘word of mouth’ effect. Demand for High Tariff A and Medium Tariff hotel rooms is estimated to have experienced negative annual growth in 2009 due to the influence of the financial/economic crisis, whereas that for High Tariff B hotel rooms is thought to have grown in 2009 after having decreased in 2008. The demand for tourist guesthouse rooms is expected to be the least affected by the crisis. Overall demand is predicted to recover gradually from 2010 onwards.     

AN EVALUATION OF ALTERNATIVE APPROACHES TO MARKET SHARE ANALYSIS WITH APPLICATION TO THE KUWAITI POULTRY MARKET

Changes in exporter market shares in the Kuwaiti poultry import market over the period 1971–81 are analysed by three modelling procedures. Two traditional approaches, a first-order constant transition probability Markov model and a set of market share equations, are found to be of only limited use. As an alternative, a multinominal logit model of market share behaviour is estimated. The empirical results indicate that, in addition to relative price changes, domestic policy inducements for Brazilian and European Community exports are important in determining market share changes. A comparison of the explanatory abilities of the models suggests that the multinominal logit model is as least as good as traditional modelling alternatives with respect to econometric criteria. It is concluded that the multinominal logit model offers considerable promise as a tool for market share research.     

Profit maximisation vs. agency: an analysis of charitable giving by UK firms

The charitable giving of a large sample of publicly quoted UKfirms is analysed within a model that explores the profit maximisationand managerial utility enhancement motives for giving. The empiricalmethod draws a distinction between the decision to participatein giving and the determination of the amount of corporate contributions.Firm size and advertising intensity are found to be positivelyassociated with the probability of participation in giving.Stricter corporate governance and the rate of directors' remunerationare negatively related to the probability of participation.Among givers, the rate of giving is related positively to R&Dintensity, the rate of directors' remuneration, and corporateprofitability and negatively to firm indebtedness.     

The role of (non-)transparency in a currency crisis model

This paper extends the work by Morris and Shin (Am. Econom. Rev. 88 (1998) 587-597) where multiple equilibria in the self-fulfilling currency attack models can be reduced to a unique equilibrium when agents observe fundamentals privately with small errors. We find that under a more general specification with realistic parameters, noisy private observations are generally insufficient to prevent the multiplicity of equilibria. The pivotal role played by the transparency of fundamentals/policies in currency crisis is also examined. Surprisingly, transparency may trigger rather than eliminate currency crises when fundamentals are relatively healthy. Our results may be relevant to research in other coordination problems.     

A quantitative approach to stakeholder interests

In an age when the role, behaviour and performance of the firm are under greater scrutiny than ever before, it is important to pursue quantitative empirical research in the area of social auditing. Using data relating to one particular company, the various inputs and outputs attributable to the firm's different stakeholders, (shareholders, employees, consumers and the community) are examined and corresponding rates of return calculated. Considerable methodological and data problems arise and consequently the results need careful interpretation.     

OR vice: Forecasting and classification techniques for narcotics interdiction

Counternarcotics interdiction efforts have traditionally relied on historically determined sorting criteria or “best guess” to find and classify suspected smuggling traffic. We present a more quantitative approach which incorporates customized database applications, graphics software and statistical modeling techniques to develop forecasting and classification models. Preliminary results show that statistical methodology can improve interdiction rates and reduce forecast error. The idea of predictive modeling is thus gaining support in the counterdrug community. The problem is divided into sea, air and land forecasting, only part of which will be addressed here. The maritime problem is solved using multiple regression in lieu of multivariate time series. This model predicts illegal boat counts by behavior and geographic region. We developed support software to present the forecasts and to automate the process of performing periodic model updates. During the period, the model was in use at. Coast Guard Headquarters. Because of deterrence provided by improved intervention, the vessel seizure rate declined from 1 every 36 hours to 1 every 6 months. Due in part to the success of the sea model, the maritime movement of marijuana has ceased to be a major threat. The air problem is more complex, and required us to locally design data collection and display software. Intelligence analysts are using a customized relational database application with a map overlay to perform visual pattern recognition of smuggling routes. We are solving the modeling portion of the air problem using multiple regression for regional forecasts of traffic density, and discriminant analysis to develop tactical models that classify “good guys” and “bad guys”. The air models are still under development, but we discuss some modeling considerations and preliminary results. The land problem is even more difficult, and data collection is still in progress.     

Causal ordering and ‘The bank lending channel’

The bank lending channel implies the Federal Reserve can influence real income by controlling the level of intermediated loans. Using the notion of causality developed by Simon (1953) and the causal order methodology developed by Hoover (1990), I test for an operative bank lending channel in the transmission mechanism of monetary policy. I find loans did cause real income; there is evidence that a bank lending channel did exist in the 1960s. The data appears to show, however, that by the early 1990s the bank lending channel was no longer operative. © 1998 John Wiley & Sons, Ltd.