Empirical evidence, observed across a variety of service industries, indicates that customers who have experienced problems with service suppliers are often dissatisfied with the ways in which problems are resolved. For example, an early study revealed that only 30-53% of customers who experienced problems with one of seven services they purchased were satisfied with the resolution (Andreasen and Best, 1977). In more recent research, only 50-67% of customers who experienced difficulties with one of five service companies were satisfied with the outcome (Berry and Parasuraman, 1991). Furthermore, it appears that, while marketing of products and services differs in many ways, customer satisfaction with services is particularly tied to the resolution of problems. Since word-ofmouth (WoM) regarding problem resolution can be a major positive or negative force in building a firm's reputation and retaining customers (Reichheld and Sasser, 1990), the reward to companies which resolve problems to the customer's satisfaction appears to be very high (Hart et al., 1990).Given the acknowledged importance of service recovery, it is surprising that so few large-scale field studies have focussed on this topic; as Kelley and Davis (1994) succinctly state: "…A dearth of empirical research confines any theoretical discussion to anecdotal reports" (p. 52). This study examines the relative importance of service recovery activities in determining overall satisfaction and consequent behavioral intentions. Data from a large field study are analyzed to address the research questions. Service recovery Dimension typesSimilarly, WoM has been identified in past research as an important postpurchase behavior for several reasons (Day, 1980). WoM communication provides face-to-face, often vivid information that is highly credible. This information can influence others' beliefs about a particular firm, and their intentions to purchase from the firm. There is also evidence 16 JOURNAL OF SERVICES MARKETING VOL. 9 NO. 1 1995 Service recovery effortWord of mouth Post-delivery claims personnel Key research questions 20 JOURNAL OF SERVICES MARKETING VOL. 9 NO. 1 1995 Encouraging complaints Reevaluating budget allocationsThe present study provides evidence of the importance of service recovery in producing satisfied customers who intend to use the firm's services in the future, and would provide positive word of mouth. Service recovery was found to be even more important than the original service failure that led to the service recovery interaction. Firms can and should use service failures to identify service system problems, reduce customer defections, and increase loyalty and positive word of mouth.
This article proposes the Immigrant Business Enterprises Classification Framework to organize immigrant‐owned businesses into categories associated with different levels of business integration into a host country's mainstream business community. The article applies the framework and reports the findings of structured face‐to‐face interviews with 199 Hispanic business enterprises (HBEs) in Indianapolis. The authors find Hispanic‐owned businesses hold different characteristics depending upon the integration category in which they are classified; the findings suggest that to support immigrant entrepreneurship, governments, business development organizations, and researchers should address category‐specific challenges, opportunities, and needs. © 2010 Wiley Periodicals, Inc.
A framework is proposed for analyzing the consumption culture, including its micro/personal and macrolexternal covariates and critiques. Two main questions emerge: (1) Are goods assigned excess meaning, such that their pursuit distracts an individual from achieving true meaning in life? (2) Does the consumption culture destroy collective values and detract from issues in the public domain?
Bayesian network methodology is used to model key linkages of the service-profit chain within the context of transportation service satisfaction. Bayesian networks offer some advantages for implementing managerially focused models over other statistical techniques designed primarily for evaluating theoretical models. These advantages are (1) providing a causal explanation using observable variables within a single multivariate model, (2) analysis of nonlinear relationships contained in ordinal measurements, (3) accommodation of branching patterns that occur in data collection, and (4) the ability to conduct probabilistic inference for prediction and diagnostics with an output metric that can be understood by managers and academics. Sample data from 1,101 recent transport service customers are utilized to select and validate a Bayesian network and conduct probabilistic inference.
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