European Journal of Marketing, Vol 28 Issue 10 Date 1994 ISSN 0309-0566
Using Neural Network Analysis to Evaluate Buyer-Seller Relationships
Barry Wray
University of North Carolina at Wilmington, Wilmington, USA
Adrian Palmer
Leicester Business School, De Montford University, Leicester, UK
David Bejou
University of North Carolina at Wilmington, Wilmington, USA.
Keywords: Consumers, Financial services, Neural networks, Relationship marketing
Type of Article: Survey
Conceptual arguments favouring a relational rather than a transactional approach to the study of buyer-seller relationships are now well understood. However,
attempts to quantify the factors contributing towards relationship quality have been held back by the complexity of the underlying factors and their interrelatedness.
Traditional regression techniques are not effective in analysing data with high levels of multi-collinearity and missing information, typical in many studies of buyer
behaviour. Makes use of a relatively new technique – neural network analysis – to try to quantify the factors contributing to buyer-seller relationship quality. The
technique uses a statistically-based learning procedure modelled on the workings of the human brain which quantifies the relationship between input and output
variables through an intermediate "hidden" variable level analogous to the brain. For this study, a neural network was developed with two outcome components of
relationship quality (relationship satisfaction and trust), and five input antecedents (the salesperson¢s sales orientation, customer orientation, expertise, ethics and the
relationship¢s duration). In a comparison of multiple regression and neural network techniques, the latter was found to give statistically more significant outcomes.
New applications within marketing for neural network analysis are being found. Contributes towards the development of the technique and suggests a number of
further possible applications.
Quality Indicators: Readability**, Practice Implications*, Originality**, Research Implications*
Introduction
Relationship marketing has been hailed by some as a paradigm shift which moves attention away from individual
buyer-seller transactions towards ongoing relationships. Instead of adopting a warfare approach to the bargaining of each
transaction, buyers and sellers become interrelated and achieve shared goals through patterned relationships with one
another[1]. In an ongoing relationship, promises are exchanged and relationships developed on the basis of trust[2].
Of course, there is nothing new in the way that organizations have sought to develop ongoing relationships with their
customers. Before the advent of large-scale production systems and mass marketing, producers of goods were able to know
each of their customers individually and able to suggest appropriate product offers. They also knew what level of credit to
trust them with. Mass production inhibited the development of ongoing personal relationships. More recently, the
development of powerful user-friendly databases has allowed organizations to recreate relationships with their customers –
a computer can now know what a business owner knew in his or her head, allowing relationship development
responsibilities to be given to a large number of staff within an organization. Some have argued that in markets where
companies offer similarly high levels of product quality, the quality of an ongoing relationship becomes a means of gaining
competitive advantage[3].
Relationship marketing strategies are not appropriate to all buyer-seller interactions. They are most appropriate where
purchases involve a high level of risk and a relationship acts as a manager of risk exposure[4]. Relationships are often a
necessity where the stream of service benefits is produced and consumed over a period of time or where they can reduce the
transaction costs associated with repeated purchase routines[5].
To suppliers of services, the development of strong relationships helps to build loyalty from customers whose loyalty is
challenged by competing suppliers. Retaining existing customers rather than expensively seeking new ones can have a major
impact on profitability[6].
Although the increasing importance of relational as opposed to discrete exchange has triggered considerable research into
the quality of buyer-seller relationships, measurement of the concept remains poor. Previous research has produced
conceptual models of the antecedents of relationship quality, or quantified simple relationships based on one or two
components at a time.
In reality, the components of relationship quality are complex, with significant interaction between the contributory
components. Attempts to isolate a few of these components at a time have generally failed to recognize interdependence and
to give clear indications of the relative importance of each factor in contributing towards a higher-order relationship quality
construct.
Much of the weakness of previous research can be attributed to the use of regression techniques which require a prior
specification of input and output relationship variables. A number of multivariate techniques, such as Canonical Correlation,
have been developed which do not presuppose relationships other than to identify which are the dependent and independent
variables. An alternative approach – which is adopted in this article – is the use of neural network analysis. Such a
methodology does not require this prior knowledge, since the network "learns" hidden relationships in data implicitly.
Linear, non-linear, interactions and multi-colinearity relationships are thus discovered and automatically assimilated into a
network of relationships. In the context of buyer-seller relationships, the model is capable of learning the relationship
between a salesperson¢s actions and customers¢ perceptions of relationship quality. The neural network is then able to map
any variety of seller¢s actions and link them to a customer response (i.e. it predicts outcomes on the basis of inputs).
This article describes an application of neural network analysis to the study of relationship quality in the financial services
sector, a sector which has seen significant development of relationship marketing strategies. First, it outlines previous
studies of relationship quality, then develops a neural network to analyse data from a survey of customers of financial
services¢ intermediaries. The results of the neural network analysis are finally compared with those obtained from a more
traditional regression analysis.
Previous Studies of Relationship Quality
There has now been considerable research into the factors that hold buyers and sellers together in an ongoing relationship.
Emerging from general models of buyer behaviour, a number of theoretical and empirically-based models have been
developed to explain the processes of interaction between buyers and sellers, both in the industrial sector[7,8,9,10], and
more recently for consumer markets[11,12]. Studies of relationship quality have drawn heavily on the social psychology
literature of interpersonal relationships, for example in explaining trust[13,14] and conflict resolution[15,16]. Analogies
have been drawn between buyer-seller relationships and relationships within families[17].
The development of ongoing relationships between an organization and its customers can involve a wide range of
operational and marketing staff and satisfactory delivery of a service has been seen as pre-requisite to the development of a
relationship[18,19]. However, particular attention has been given to the relationships which develop between buyers and
sales personnel. For some low-contact services, such as financial services, customers¢ most important contact with an
organization may be through its sales personnel.
Previous research into sales persons¢ effectiveness has concentrated on a static analysis of the behavioural, environmental
and organizational factors that influence performance. The relatively static nature of much of this analysis has been followed
by analyses which see the sales person as a dynamic processor of information, analyses in which interaction between
customers and sales personnel results in the development of long-term relationships[12].
A number of studies have identified individual components of relationship quality, using concepts and measurement devices
used in social psychology. A comprehensive model of relationship quality has been proposed by Lagace et al.[20] which
conceptualized two components of quality – trust in a salesperson and satisfaction in the relationship so far. From the
literature, a number of antecedents of these components have been identified, including sellers¢ customer
orientation/empathy, sellers¢ expertise, sellers¢ ethics, the degree of sales orientation shown by a salesperson, and the
duration to date of the relationship.
A number of studies have sought to conceptualize relationship satisfaction. Three dimensions have been attributed to it by
Crosby and Stevens[19]: satisfactory interactions with personnel; satisfaction with the core service (the extent to which a
service satisfies customers¢ needs), and satisfaction with the organization. In a study of life insurance customers, satisfaction
with the core service was found to be closely related to satisfaction with the contact person and the organization.
An important objective of relationship marketing strategies is the development of trust, which has been seen as having a
crucial function in a relationship in allowing tensions to be worked out[14]. Its development results in the exchange of
promises being perceived by both buyer and seller as more important than short-term transactional exchange. Trust
facilitates joint problem solving.
Some studies have sought to measure the extent to which sales personnel exhibit customer orientation[21,22]. Sales
personnel who are customer-oriented "practice the marketing concept at the level of the individual salesperson and
customer"[21]. Such sales personnel are able to empathize with customers and are concerned about satisfying their needs
better than would their competitors, in contrast to sales-oriented personnel who attempt to create demand for their services
with only a secondary regard for the needs of their customer. Saxe and Weitz[21] have analysed customer orientation in
terms of two factors: "relations" and "ability to help". The former refers to the abilities of sales personnel to develop
long-term relationships with customers on the basis of trust, co-operation and conflict resolution, while ability to help refers
to "the ability of salespeople to help their customers satisfy their needs".
The opposite of a salesperson¢s customer orientation is a sales orientation. Many salespeople prefer to sell hard what they
know best, rather than taking trouble to identify customer needs[21]. A sales orientation may be present where the
salesperson fails to diagnose a clients¢ product requirements[23] or the processes by which the client wishes to evaluate
alternatives[24]. Customers may perceive a sales orientation as a form of pressure, and relationship development may be
inhibited by customers¢ continuing suspicion that the seller is paying too much attention to his or her own selling needs,
rather than to their needs as customers.
The inability of many customers properly to evaluate complex high-credence goods and services can put them at the mercy of
sales personnel. Without the technical knowledge necessary to judge salespersons¢ claims, buyers seek sales personnel who
act in an ethical manner. A preoccupation of sales personnel with short-term goals may result in unethical behaviour which
could subsequently endanger the development of long-term buyer relationships[25]. The importance of customers¢
judgements of sales personnel¢s ethics in evaluating relationship quality has received much recent attention[20,29]. It has
been suggested that consumers¢ assessment of a seller¢s ethics are based on the seller¢s past ethical behaviour and their
expectation of future ethical behaviour, based on both personal and societal norms[29].
The effect of sales personnel¢s levels of expertise on sales performance has been researched extensively. Sellers¢ expertise
has had a number of elements attributed to it including: their measurable technical knowledge; their ability to demonstrate
such knowledge and competence; proof that they are expert in their field (e.g. through formal qualifications); and an explicit
statement of availability, ability and capacity to serve the customer[26]. Credibility, reliability, responsiveness and an
ability to get answers were seen as important determinants of a salesperson¢s competence by Hayes and Hartley[27], in
contrast to aggressiveness and persuasiveness, which detracted from it.
Finally, the duration of a relationship has been cited as a factor explaining the quality of a relationship. This is particularly
true in the development of trust, for which Swan and Nolan[28] identify three stages of development. In the first stage, there
has been no opportunity for exploration of each parties¢ credentials; therefore the level of trust between buyer and seller is at
a minimum. Once exchanges have occurred, trust development moves into the second stage, in which the buyer has the
opportunity to check the actual delivery of a service against the promises that the seller has made. Trust is established in the
third stage, where the perceived performance matches the promised performance.
Neural Network Approach to Measuring Relationship Quality
Previous studies of the factors contributing to relationship quality have produced mixed results in terms of the emerged
constructs and the levels of significance obtained. Furthermore, these have tended to give little indication of the relative
importance of each construct in contributing to overall relationship quality or the interaction between underlying factors.
Where regression analysis has been used, models can only be run by a prior specification of the relationship between
dependent and independent variables. By limiting a model to this set of relationships, other, possibly statistically significant,
relationships may be missed.
Many of the constructs which have emerged from previous studies are interdependent: for example ethical behaviour
contributes towards trust and relationship satisfaction, while trust itself can be seen as contributing towards the assessment
of sales personnel¢s ethical credibility. It would be more appropriate to see these constructs in terms of an interdependent
network of influencers of relationship quality.
In analysing ongoing buyer-seller relationships, neural network analysis has three primary advantages over regression
analysis:
1.Neural network development does not require knowledge of the underlying relationships between the input and output
variables (both linear and non-linear), since the network "learns" relationships hidden in the data. These complex
relationships are discovered and automatically assimilated into the weights connecting the nodes of the network. These
weights contain the "learned information" from the network training phase and are analogous to regression coefficients.
2.The associative abilities of neural networks make them more robust to missing and inaccurate data, since the
knowledge of relationships between variables is distributed across numerous network connections. Regression, on the
other hand, cannot tolerate missing data and works poorly with inaccurate data since all relationship knowledge is
stored in a single beta coefficient.
3.Neural networks¢ performance is not diminished by the multi-collinearity problem of regression analysis. Non-standard
conditions, violations of assumptions, high influence points, and transformations can all be handled by the neural
network model.
As well as learning the relationship between a salesperson¢s actions and customer¢s reactions, the neural network approach
to predicting satisfaction and trust also provides the user with the ability to identify factors which have a significant impact
on the customer¢s perception of the salesperson. In this manner, the neural network is both a predicting tool and a
factor-screening tool.
There are two principal phases in neural network analysis: "learning" and "predicting." During the learning, or training,
phase the network "learns" by adjusting the weights between its nodes. The input data must be presented to the network many
times. Data are split into two files. The first is used to train the network and the second file (the recall set) is used as a test
of the network¢s predictive ability. During the training phase the network weights are "saved" at many intervals and tested to
see how well the network can predict outcomes using the weights it has learned up to that point. Following thousands of
iterations, convergence occurs and the best weights for each element of the network can be derived.
Methodology
Sample Frame
The sample for this study was drawn from customers of financial services¢ intermediaries. The financial services sector
presents a good opportunity to study buyer-seller relationships and the sector has been at the forefront of the development of
relationship marketing strategies, for a number of reasons. Customers¢ perception of the riskiness of financial services
purchases can allow a relationship to be used as a means of managing their perceived risk exposure. The longevity of many
financial services entails some form of relationship continuing to exist between an organization and its customers and the
sales personnel are often the only people within the organization with whom a customer will have dealings. Relationships
are attractive to buyers because high transaction costs can occur where a portfolio of financial services is transferred from
one provider to another. Finally, the intangible and often incomprehensible nature of financial services encourages buyers to
judge the ethical credibility of sales personnel.
Instrument Development
A structured questionnaire comprising three sections was developed for this study. The first group of questions related to the
demographic and socio-economic characteristics of respondents; for example, their age, income level, gender and race. The
second group of questions sought information about the behaviour of respondents in relation to financial services; for
example, the types of financial services that they had purchased (e.g. stocks, bonds, etc.) from a financial broker and the
length of their relationship with the broker. The final group of questions sought to elicit respondents¢ attitudes towards their
broker. For this, a set of questions was developed based on the SOCO scale items[21] and other scales. The SOCO scale
has been used previously to measure the extent of personnel¢s sales orientation and customer orientation, and a number of
studies have replicated its results[22].
Seven emerged constructs were used in the neural network analyses:
1.the level of sales orientation/pressure the customer perceived in the broker (selling);
2.the perceived level of the broker¢s customer orientation (customer);
3.the perceived ethical standards of the broker (ethics);
4.the broker¢s perceived expertise (expertise);
5.the customer¢s trust in the broker (trust);
6.the relationship¢s duration (duration);
7.the customer¢s overall satisfaction (satisfaction).
Except in the case of relationship duration, response categories ranged from 7 "strongly agree" to 1 "strongly disagree" (for
relationship duration, response categories ranged from less than a year to three years and more). The selling, customer and
ethics scales were drawn from items of the SOCO scale. Some of the items were worded negatively to reduce response bias
and the scores of these items reversed in the data analysis. Simple unweighted summations of the scores of the scales were
used in the artificial neural network analyses. The reliability of the emerged constructs are shown in Table I . Reliability
Analysis of Scale Items of Emerged Constructs . The duration, expertise, satisfaction and trust scales were indicated by
single-item questions.
Data Collection
A telephone survey was conducted in 1992 in four south-eastern cities of the United States, using a randomly selected
sample based on telephone numbers. Interviewers were asked to interview the adult member of the household who was most
involved in the purchase of financial services. A pilot survey had achieved an acceptable level of response. The problems
of using telephone surveys to collect personal information were recognized, and respondents were asked to answer only
those questions that they felt comfortable with. Out of 1944 interviews, 564 usable questionnaires were available (a
response rate of 29 per cent). A total of 280 callbacks were made for verifications.
Development of the Neural Network
A neural network was developed using the InstaNet submenu in NeuralWorks Professional II. The standard back
propogation configuration suggested by Rumelhart[30] was used. The heteroassociative neural network uses generalized
delta rule learning with five nodes in the input layer, 12 nodes in the hidden layer and two nodes in the output layer. The
input layer had one node for each of the antecedent constructs described above (expertise, duration, selling, customer and
ethics).
The output layer had two nodes for relationship quality components:
1.customer satisfaction with their relationship (satisfaction);
2.customer¢s trust in the seller (trust).
The number of nodes in the hidden layer is based on the rule of thumb that a good guess is roughly twice the number of nodes
in the input layer plus two. A conceptual model of the neural network is shown in Figure 1 . Conceptual Model of the Neural
Network .
A total of 564 survey responses were available and these were randomly split into two files, each of 282 observations. The
first file was used to train the network and the second file (the recall set) was used as a test of the network¢s predictive
ability. During the training phase the network weights were "saved" at many intervals and tested to see how well the
network was able to predict the customer¢s satisfaction and trust based on the weights it has learned up to that point.
"Prediction" involves two separate measures – customer satisfaction and the customers¢ trust in their broker. The difference
between the predicted level of satisfaction and the actual observed level is calculated for every data point in the recall set.
For instance, if the neural network predicted the level of satisfaction to be 4.3 and the actual observed satisfaction is 5.2,
then the absolute difference for that one observation is 0.9. This absolute difference is averaged over all data points to
provide an overall measure to compare between networks. The same is done for the trust variable. The best performance for
predicting satisfaction for the network is an average difference of 0.5760 when the network is trained for 400,000
replications. The best performance for predicting trust for the network is an average difference of 0.5927 when the network
is trained for 400,000 replications. The 282 data points were shown to the network around 1,418 times each to get the best
performance possible. It should be reiterated that the network was predicting for 282 data points not used in network
training.
Validation of the Neural Network
To compare the performance of an artificial neural network to linear regression, a regression equation was computed from
the same data used for training the neural network. Two separate regression equations are necessary because of the two
different output variables – satisfaction and trust. The equations were then used to predict satisfaction and trust from the
same recall data set used to evaluate the neural network. The performance of each approach was tested to determine which
tool is the better predictor. Using the SAS REG procedure, the least squares multiple regression model computed for
predicting customer satisfaction was:
y = 3.120 + 0.3570 × expertise - 0.0333 × duration
- 0.2250 × pressure + 0.3174 customer - 0.1362 ethics
The multiple regression model¢s best performance is an average difference of 0.6098 from the actual rating for customer
satisfaction, whereas it was noted above that the neural network¢s performance was 0.5760. To test the significance of the
difference in predictive ability of the two models, a matched sample pairs statistical procedure was used to test the
hypothesis that the mean difference between the models is zero (i.e. there is no difference between the predictive abilities of
the two models). The test statistic for the t-test is -1.69. The probability of a greater absolute value for this statistic under the
null hypothesis that the population mean is zero (there is no significant difference between the predictive ability of the two
models) is 0.0928. This p-value is evidence that the neural network outperforms the regression model for predicting
customer satisfaction.
The least squares multiple regression model computed for predicting customer trust is:
y = 4.2509 + 0.3132 × expertise + 0.0364 × duration
- 0.3526 × pressure + 0.1599 customer - 0.1252 ethics
The multiple regression model¢s best performance is an average difference of 0.6459 from the actual rating for customer
satisfaction, compared to the neural network¢s performance which was 0.5927. A matched sample pairs statistical procedure
was again used to compare the predictive abilities of the two approaches. The test statistic for the t-test was -2.42 and the
probability of a greater absolute value for this statistic under the null hypothesis that the population mean is 0 is 0.0160.
Again, the small p-value is evidence that the neural network outperforms the regression model for predicting customers¢
level of trust in their broker.
Analysis of the Relative Importance of Factors Contributing to Satisfaction and Trust
The contribution of each of the input factors to relationship quality was determined in the following way.
First, if the neural network¢s ability to predict is observed to be unchanged by removing a factor, the factor should be
"trimmed" (left out of the process). Second, when the neural network¢s predictive ability is significantly reduced by
eliminating a single factor, the factor is contributing unique and valuable information and should be kept. If a factor is
critical in determining the level of customer satisfaction and/or trust, an investigation is warranted to determine why the
factor¢s influence is high. Third, factors having a negative impact on network performance, actually hindering the network¢s
ability to predict by presenting confounding information, should be excluded.
Results
A procedure analogous to step-wise regression was used to investigate the significance of each determinant of customer
satisfaction and trust, the two output components of relationship quality.
Impact of Seller¢s Actions on the Buyer¢s Perceived Level of Satisfaction
To investigate the importance of each of the factors – expertise, duration, selling, customer and ethics – to satisfaction, the
following six-step procedure was used:
1.A new neural network was constructed with one less input processing element using the same paradigm as the full
model (learning rule, transfer function, number of nodes in the hidden layer, etc.)
2.A column of data was eliminated, corresponding to a single factor from the training data set and recall data set.
3.The new network was trained using the "trimmed" file.
4.A recall was performed using the trimmed recall data set.
5.The performance of the trimmed network was computed.
6.Each of steps 2-5 were repeated for each of the five factors using the same basic model as step 1.
Initially each network was trained for 800,000 replications, pausing at intervals of 50,000 replications to save the network
weights. The decision to end the training process was based on the convergence of network learning as before. The optimum
performance of each network at the end of the training process (minimum average deviation from optimal cost) is used to
compare the difference of networks. In order to determine whether the difference in predictive ability of the six networks
(the full model with all five factors and the five models with one factor trimmed from each) is significant, a randomized
block design test was used. The treatments are the elimination of individual factors, while the blocks are the set of survey
results. Since there are six models to test, a two-way analysis of variance (ANOVA) without interaction was considered the
most appropriate statistical test (Table II . ANOVAs for Variables Satisfaction and Trust ). All six distributions exhibit very
similar non-normal characteristics. The result of a test for normality using PROC UNIVARIATE is a p-value < 0.0001.
Since the normality assumption is violated, it was necessary to use a technique to test for homogeneous variance that does
not rely on normality. A rank transformation approach[31] was used to deal with the normality problem. This procedure is a
valid, powerful, and easily implemented non-parametric alternative to the (parametric) ANOVA approach which does not
require homogeneous variance and alleviates the need for a test of homoscedasticity. A non-parametric analysis was created
by transforming the data into ranks and then using the ranks in a parametric ANOVA procedure.
In order to compare the predictive ability of different networks, a file was created containing one observation for each block
within each treatment. The deviations from optimum for all five trimmed networks as well as the full network were used.
The two-way ANOVA analysis revealed a significant difference between the predictive ability of at least one of the six
models (treatments) and at least one block. The F-value of 17.32 is strong evidence (p-value < 0.0001) of a treatment effect
(the treatment being the elimination of one factor from the full model). The significant F-test suggests that at least one of the
factors included in the study had a significant impact on the model¢s predictive ability. The F-test for the blocking factor, the
different survey results, also has a significant impact on predictive ability. The F-value of 17.49 is strong evidence (p-value
< 0.0001) that blocking is important.
In order to compare the performance of each model with all other models, the Walker-Duncan K-ratio T-test procedure was
used on the rank transformed data. As a result of the use of the rank transformed data, the test is more robust and has more
power[31] than a test using the raw data from non-normal populations. The results of the test are shown in Table III . Rank
Transformation One-way Analyses for Variables Satisfaction and Trust . In this table, the networks are ordered according to
the arithmetic mean of differences from optimum.
The most notable result is that the model with all five factors included performed significantly better than the models with
one missing factor, except the model with duration missing. This indicates that all factors except duration are providing
some information important to determining customer satisfaction. The relative importance (ranking) of each factor, from most
important to least important, is as shown in Table IV . Ranking of Factors for Customer Satisfaction .
A second observation is that selling orientation is significantly more important than any other factor included in the study.
Impact of Seller¢s Actions on Buyer¢s Perceived Level of Trust
The same procedure was used to assess the importance of individual determinants on the customers¢ trust in their brokers. A
file containing the difference between the actual trust rating and the predicted trust rating for the full model and for the five
models with one factor left out was used.
The results of the ANOVA procedure for customer trust are shown in Table II . ANOVAs for Variables Satisfaction and
Trust . The two-way ANOVA analysis revealed a significant difference between the predictive ability of at least one of the
six models (treatments) and at least one of the blocks. The F-value of 14.88 is strong evidence (p-value < 0.0001) of a
treatment effect (the treatment being the elimination of one factor from the full model). The significant F-test suggests that at
least one of the factors included in the study has a significant impact on the model¢s predictive ability. The F-value of 15.00
is strong evidence (p-value < 0.0001) that blocking is important. Duncan¢s multiple range test was again used on the rank
transformed data and the results are shown in Table II . ANOVAs for Variables Satisfaction and Trust .
The key observation from the test is that all factors are contributing information significant for determining customer trust.
The relative importance (ranking) of each factor from most important to least important is as shown in Table V . Ranking of
Factors for Customer Trust .
The ranking of the factors for customer trust, however, is different from the customer satisfaction ranking.
Conclusions
Many conceptual models have been developed in the attempt to explain the processes by which salespeople develop ongoing
relationships with their customers; but there have been relatively few attempts to measure and quantify the concept of
relationship quality.
This study has indicated the usefulness of a research methodology appropriate for the analysis of complex ongoing
buyer-seller relationships. In a comparison of regression analysis and neural network analysis, the latter was significantly
better able to explain the relationship between two indicators of relationship quality (relationship satisfaction and trust) and
five of its antecedents (level of salesperson¢s customer orientation, level of salesperson¢s sales orientation, salesperson¢s
ethics, salesperson¢s expertise and the duration to date of the relationship). The neural network analysis has been able to
show that each of these factors has a statistically significant effect on the level of perceived relationship quality.
Relationship satisfaction has been shown to be most influenced by the level of sales orientation customers perceive in their
salesperson. A high level of pressure was negatively related to satisfaction, whereas expertise – which was ranked the
second most important factor – was positively related to satisfaction.
In the case of trust, the primary ranking of relationship duration confirms theories of trust development which see its
development proceeding through a number of stages in which trust only develops after the time has elapsed for a buyer to
check out the seller¢s ability to honour sales promises.
This study has been restricted to customers of financial services organizations, and replication studies would be useful.
While it is quite likely that the methodology is appropriate to a wide range of buyer-seller situations, the input and output
constructs may not be of general applicability. It may be the case, for example, that ethical credibility is perceived as being
an important sought characteristic of financial sales personnel (because customers¢ ability to evaluate their product is low),
but it may be of less relevance where a salesperson is dealing with a tangible good.
Neural network analysis is a relatively new analytical tool which has only recently begun to find marketing applications. The
results of this study suggest that it may offer superior solutions to a wide range of marketing prediction problems
characterized by complex and interdependent, causative variables. The technique has been used in direct marketing to
develop a profile based on customer characteristics of the most profitable type of customer. The technique would also
appear to have potential for predicting consumer choice and the success of site locations.
While this research has offered further insights into the complex factors underlying buyer-seller relationships, further
research would be useful in order to model alternative networks. While the networks used in this analysis used only those
inputs which had been most frequently cited in the literature, it would be useful to build alternative networks which include
factors such as mutual disclosure of information which some research has suggested may contribute towards relationship
quality.
Table I . Reliability Analysis of Scale Items of Emerged Constructs
Figure 1 . Conceptual Model of the Neural Network
Table II . ANOVAs for Variables Satisfaction and Trust
Table III . Rank Transformation One-way Analyses for Variables Satisfaction and Trust
Table IV . Ranking of Factors for Customer Satisfaction
Table V . Ranking of Factors for Customer Trust
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