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The purpose of this paper is to explore the differences and similarities between two methods/models for estimating customer equity (CE): one using behavior-based data and one using market-based data.

Two separate analyses of the same market scenario (telecom industry) were conducted, by applying the CE estimation method from Rust et al. (2004) and the CE model from Gupta et al. (2004).

Different methods/models can produce similar estimates, which corroborates the defense of an integrated multi-method approach to evaluating CE. In addition, they each provide different benefits. The behavioral data model provides identification of CE drivers and assists in the task of marketing resource allocation, the market-based data model is simple and easy to implement and is recommended in cases when CE is used as a financial indicator.

This paper contributes to the CE literature in the following ways. First, it demonstrates the possibility of obtaining similar estimates of CE using distinct types of data and data collection procedures, and with two different estimation methods/models. Second, it confirms that either model allows firms to compute the expected market capitalization at any given time using customer and financial information. Third, it demonstrates the convergent validity of these two methods/models for estimating CE for either public or private companies, thus legitimizing the comparison of their respective CE values, regardless of the type of source data or estimation formula used.

Despite prior studies, little has been done to understand the advertising carry-over effect. The purpose of this study is to investigate the heterogeneous attributes of the carry-over effect by focusing on the differences between franchise and non-franchise firms.

The data were retrieved from the Compustat database and annual corporate financial reports (10-K) for five representative franchise industries from 1980 to 2009. Ultimately, 185 firms were included and 1,592 firm-year observations were analysed. This study used a Panel VAR (Vector Autoregression) to examine the effects of advertising on firm performance. We can control endogenous effects using Panel VAR, which also allows us to control unobserved firm-specific heterogeneity.

This study found that advertising had no effect on sales growth or brand equity in the long run for non-franchise firms and further confirmed that non-franchise firms incur agency costs. In contrast, the effect of advertising on sales growth and brand equity was significant for franchise firms. A carry-over effect of advertising on brand equity was detected for franchise firms, but sales growth rapidly decreased after two years.

Even though franchise firms retain the carry-over effect of advertising on brand equity, franchisors should carefully monitor market trends and their sales growth because sales growth rapidly decreased after two years.

This study incorporated the concepts of the delayed response effect and the customer holdover effect to better understand the advertising carry-over effect. From the results, this study proposed a more detailed concept of carry-over effects for further studies. From this perspective, this study makes both academic and industrial contributions.

For several years, the classic car market has been attracting considerable media and public attention, but the research literature is virtually nonexistent. The purpose of this paper is to address the factors that determine the values of classic car models and explain the remarkable price differences among them.

The paper develops and tests a set of research hypotheses about the effects of model characteristics on market values in the context of a generalized hedonic price model that also accounts for heterogeneity among classic car brands.

It is demonstrated that classic car model values reside at several levels and are determined by observable characteristics pertaining to aesthetics, rarity, engineering and performance. In addition, we show that classic car marques play a critical role in the determination of model values and account for considerable variation in values, even after controlling for observable model attributes

This is one of the first empirical studies to address classic car model value formation. The findings reveal how measurable, observable factors determine classic car model values and augment our understanding of a very interesting but understudied market.

The purpose of this study is to present a brief overview of hospitality finance/accounting (HFA) research and to propose the utility of interdisciplinary research in the HFA field.

This study outlines HFA research and adds a brief summary of mainstream finance and accounting research topics. To further improve HFA research, this study suggests the need for interdisciplinary research that could effectively integrate finance/accounting with other management subjects in the hospitality field.

Despite its importance, interdisciplinary research has not been given enough attention in the field of HFA. This study sheds light on the need for interdisciplinary research and proposes paths for conducting interdisciplinary HFA research, such as behavioral finance, marketing-finance interface, human resource management finance/accounting, etc.

This study suggests that the results of interdisciplinary HFA research can provide useful practical implications from shareholder and organizational perspectives in the hospitality industry.

Although the interdisciplinary research concept is not really new, it has not been extensively addressed in hospitality academia. In this respect, this study suggests expanding the horizon for HFA researchers.

This paper sets out to compare different methodologies for measuring the value(s) of live popular music and to explore the different motivations amongst a range of organisations engaged in that work.

The authors analyse how the values of live music are measured, who does it and why. Based on this analysis the authors present a model that visualises the myriad of organisations, methods, aims and objectives involved.

The authors identify three approaches to measuring the impact of live music (economic impact studies, mapping and censuses and social sciences and humanities) and three types of actors (industry, policy and academia). The analysis of these demonstrates that measuring live music is not a neutral activity, but itself constructs a vision on how live music ecologies function

For cultural organisations, demonstrating the outcomes of their work is important in acquiring various forms of support. The model presented in this paper helps them to select adequate methodologies and to reflect on the consequences of particular approaches to measuring live music activities.

While the number of studies measuring live music's impact is growing, theoretical and methodological reflection on these activities is missing. The authors compare the different methodologies by discussing strengths and weaknesses. This results in a model that identifies gaps in existing studies and explores new directions for future live music research. It enhances understanding of how different ways of measuring live music affect policymaking and conceptions of what live music is and should be.

The purpose of this study is to model the consumer journey of admission-based membership services from initial purchase to full-season memberships. Particularly, the study pays attention to customer-owned contacts (purchase behavior) and service-owned contacts (salesperson voice- and text-based communications), to examine longitudinal internal data to determine factors which hinder and propel customers toward full memberships.

On the basis of big data supplied by a National Hockey League team, the study uses three simultaneous equations in modeling to account for potential endogeneity related to the likelihood that sales and service personnel are more likely to contact frequent customers. The longitudinal data allow us to map the customer journey over the course of multiple years, compared to typical cross-section studies.

The findings show that as customers increasingly own committed points of contact, they are prepared to move to the next level – but rarely skip major steps in the relationship journey. The quantity, type and timing of customer contacts by the service firm may hinder or propel the customer down the path to purchase full memberships.

The prevalence of big data among service firms should allow researchers to better understand how consumers respond to contact strategies over time, as well as fluctuations in firm performance. The research adds to the customer journey research stream, while meeting the call of researchers to bridge the gap between service marketing research priorities and current practice.

Sales practices and marketing automation tactics may come at the cost of burning leads and alienating future members. Frequent text-based contacts absent voice-based interactions hinder consumer journey and work against relationship building. Service marketers can learn how to better allocate resources, properly manage and motivate contact strategies and target campaigns to send the right message via the right media at the right time.

This is the first study to map customer journey for admission-based, membership services. The longitudinal approach across multiple years provides a deep understanding of how customers take steps toward loyal membership status, while also pinpointing potential drawbacks of current contact strategies.

This study aims to examine consumers’ responses to crowdsourcing campaigns in the request initiation stage using the signaling theory from economics. The purpose of the research is threefold. First, it provides a comprehensive classification of various task types within crowdsourcing. Second, it conceptualizes crowdsourcing announcements as signals of customer orientation and empirically tests the differential effects of the two most common crowdsourcing task types (product- and communication-related) on customer orientation perceptions. Third, it illuminates the downstream behavioral consequences of crowdsourcing campaign announcements.

The authors conducted secondary data analysis of 883 crowdsourcing campaigns (pilot study) to provide evidence on the differential effects of crowdsourcing task types. In addition, four laboratory experiments were conducted to test the theoretical arguments. To test the main effect of crowdsourcing task types, Study 1A (N = 252 MTurk workers) used a one-factor (product- vs communication-related crowdsourcing vs control) between-subject design, whereas Study 1B (N = 171 undergraduate students) used a 2 (task type: product- vs communication-related) by 2 (product category: restaurant vs fashion) between-subject design. Study 2 (N = 93 MTurk workers) explored the underlying mechanism using a one-factor (product- vs communication-related) between-subject design. Study 3 (N = 375 MTurk workers) investigated the boundary condition for the effect of task type with a 2 (task type: product- vs communication-related) by 3 (company credibility: low vs neutral vs high) between-subject design.

The pilot study provides evidence for the conceptualized typology and the differential effects of crowdsourcing task types. Study 1A reveals that product-related crowdsourcing tends to have a more substantial impact than communication-related crowdsourcing on how customer-oriented consumers perceive a company. Study 1B validates the results of Study 1A in a different product category and population sample. Study 2 shows that the differential customer-orientation effect is mediated by the perceived cost of implementing the crowdsourcing outcome and unravels the differences in consumers’ purchase and campaign participation intentions depending on task type. Study 3 highlights that the customer-orientation effect attenuates as company credibility increases.

This research contributes to the crowdsourcing literature by categorizing the various types of crowdsourcing campaigns companies undertake and revealing the differential impact of the different types of crowdsourcing campaigns on consumers’ perceptions and behavioral intentions. In doing so, this research converges two lines of consumer research on crowdsourcing, i.e. product- and communication-related crowdsourcing. The findings add to the debate over the returns from research and development (R&D) versus advertising and extend it from marketing strategy to crowdsourcing literature.

The findings highlight the importance of choosing specific task types for crowdsourcing and lead to practical recommendations on designing crowdsourcing campaigns to maximize their benefits to crowdsourcing brands.

To the best of the authors’ knowledge, this is the first study that differentiates crowdsourcing task types and compares their effectiveness from a consumer perspective.

This paper aims to investigate the impacts of country-of-origin (COO) cues, country-related affect (CRA) and country-related product associations (CRPA) on consumers’ intention to purchase hotel services.

A survey of 305 respondents was conducted. Mediation and conditional process tests were performed to examine the proposed theoretical framework.

The results suggest that CRA is positively correlated with consumer trust, which, in turn, affects purchase intention. For consumers with a high (vs low) level of consumer ethnocentrism (CE), the effect of CRA on trust is weaker. CRPA is positively correlated with both consumer trust and price perception, which, in turn, affect purchase intention. For consumers with rich subjective knowledge (SK) of hotel services, the effect of CRPA on price perception is weaker. The effects of COO stereotypes and the moderation effects of CE and SK hold after controlling for consumers’ age, gender, income, education and objective knowledge.

Hospitality practitioners can make use of different types of country-related information to communicate effectively with consumers in a global environment.

This research is the first to discover the different mechanisms underlying different types of COO and the boundary conditions on these effects.

The purpose of this research is to investigate whether inclusion of risk assessment variables in the multiple discriminant analysis (MDA) model improved the banks ability in making correct customer classification, predict firm's performance and credit risk assessment.

The paper reviews literature on the application of financial distress and credit scoring methods, and the use of risk assessment variables in classification models. The study used a sample of 56 performing and non‐performing assets (NPA) of a privatized commercial bank in Tanzania. Financial ratios were used as independent variables for building the MDA model with a variation of five MDA models. Different statistical tests for normality, equality of covariance, goodness of fit and multi‐colinearity were performed. Using the estimation and validation samples, test results showed that the MDA base model had a higher level of predictability hence classifying correctly the performing and NPA with a correctness of 92.9 and 96.4 percent, respectively. Lagging the classification two years, the results showed that the model could predict correctly two years in advance. When MDA was used as a risk assessment model, it showed improved correct customer classification and credit risk assessment.

The findings confirmed financial ratios as good classification and predictor variables of firm's performance. If the bank had used the MDA for classifying and evaluating its customers, the probability of failure could have been known two years before actual failure, and the misclassification costs could have been calculated objectively. In this way, the bank could have reduced its non‐performing loans and its credit risk exposure.

The valiadation sample used in the study was smaller compared to the estimation sample. MDA works better as a credit scoring method in the banking environment two years before and after failure. The study was done on the current financial crisis of 2009.

Use of MDA helps banks to determine objectively the misclassification costs and its expected misclassification errors plus determining the provisions for bad debts. Banks could have reduced the non‐performing loans and their credit risks exposure if they had used the MDA method in the loan‐evaluation and classification process. The study has proved that quantitative credit scoring models improve management decision making as compared to subjective assessment methods. For improved credit and risk assessment, a combination of both qualitative and quantitave methods should be considered.

The findings have shown that using the MDA, commercial banks could have improved their objective decision making by correctly classifying the credit worthiness of a customer, predicting firm's future performance as well as assessing their credit risk. It has also shown that other than financial variables, inclusion of stability measures improves management decision making and objective provisioning of bad debts. The recent financial crisis emphasizes the need for developing objective credit scoring methods and instituting prudent risk assessment culture to limit the extent and potential of failure.