To demonstrate the strength of the Reliability driver, we pooled those florists where they gave their cut-flower supplier high ratings on Reliability (the top one-third of ratings), and pooled those florists where they gave their cut-flower supplier low ratings on Reliability (the bottom one-third of ratings).   We then computed the average satisfaction rating, loyalty percentage, and the percent of florists intending to increase purchasing from the suppliers rated for each group.   Table 2 shows the satisfaction and loyalty percentages for the high and low Reliability rating groups.

The large differences in florist satisfaction, purchasing loyalty, and intention to increase purchasing between the high and low Reliability-rating groups demonstrates the importance of this factor.   Florists value wholesaler reliability.   These results were also verified in our model analyses where the contribution of all factors collectively on florist satisfaction/ loyalty were determined, and Reliability was the top driving factor.   No other factor measured in the research (Table 1) showed as large of impact on florist satisfaction and purchasing loyalty as did the Reliability factor.

ln the survey evaluation of cut-flower suppliers, florists rated up to three supply sources (primary wholesale florist, Miami or farm-direct source, and wire-service cut-flower programs) on over 30 attributes.   Factor analysis of the attribute data identified six attributes that collectively measured Reliability, as shown in Table 3.   The percentage data in Table 3 represents the percent of florists that associate the attribute with the supply source.

For all six attributes related to Reliability, florists rated their primary wholesale florist significantly higher than ratings given to the other competing cut-flower supply sources.   In fact, wholesale florists excelled on "Courteous & professional salespeople", "Provides consistent on-time delivery", "Easy to place an order", and "Have a long business relationship with them", achieving scores of more than 80% for each attribute.   There were no attribute scores on Reliability where competing supply sources scored as high or higher than wholesale florists.   Thus, wholesale florists, on average, obtain stellar performance scores on Reliability, the factor deemed most important to florists, and the factor that most drives florist satisfaction and florist purchasing behavior.

The WF&FSA ad campaign is now currently utilizing several Reliability attributes in its advertising messages to florists.   Wholesaler Reliability is highly salient to florists, and wholesale florists excel on the performance of Reliability in their business relationships with florists.   Repeating these messages to florists with the WF&FSA ad campaign can only help to strengthen the business relationships between wholesalers and florists.

About the Florist Research.  In mid-June of 2006, survey packets were mailed to 2,000 randomly-selected retail florist shops throughout the US (1,968 surveys were deliverable to florist addresses).   Each packet contained a four-page survey "booklet", a plea for survey participation, and a monetary incentive to improve survey response rate.   By mid-July of 2006, 365 florist shops had responded to the survey, representing a 19% response rate, a more than acceptable response rate for a one-time mailing.   An evaluation of the survey response by florist size groups (annual sales) and by US regions indicated that the survey response was largely representative of the retail florist population at large.   In the survey, florists evaluated their cut flower suppliers (their primary wholesale florist, Miami or farm-direct sources, and wire-service cut-flower offerings) on over 30 attributes.   Florists also evaluated their hardgood/ gift item suppliers (their primary wholesale florist, direct sources, and gift marts) on over 20 attribute measures.   The survey findings had a sampling error of +/- 4% at the 90% confidence level, meaning that we are 90% sure that the "true score" lies within 4 percentage points of our reported survey measures.

In our previous article, we revealed that retail florists strongly value the reliability of their cut flower suppliers, and showed that wholesalers excel in their performance of reliability (Prince & Prince, 2007; also see April 17^th WF&FSA netWORK). In this article, we focus on another salient factor for florists, supplier Cut-Flower Quality, and examine how florists perceive cut-flower quality from their suppliers, and how those perceptions of quality largely define how wholesalers can best communicate and deliver quality flowers to florists. These research findings are derived from the latest WF&FSA (Wholesale Florist and Florist Supplier Association) florist research conducted to guide the WF&FSA ad campaign (see About the Florist Research).

In the cut-flower portion of the survey, florists evaluated their "primary wholesale florist" and "direct floral sources" on 33 attributes that measured the product and service offerings, and operations of the cut-flower suppliers. Since each of the florists (365 completed surveys) evaluated about two cut-flower suppliers, on average, the survey obtained over 700 florist evaluations of their cut-flower suppliers, with each evaluation comprising 33-attribute measures.

In our prior reporting to WF&FSA’s Marketing and Communications Committee (Prince & Prince, 2006), we showed the descriptive research findings indicating how each supplier source scored on each of the 33 attributes. In this reporting, we take a different approach to the data, and use Structural Equation Modeling, SEM (Byrne, 2001) to reveal the core structure underlying these evaluations, and to understand how these 33-attribute measures are related (correlated). From our analysis of how these attributes "move together", we are able to uncover the "mind set" of florists in their evaluation of their floral suppliers. Our model findings identified ten key attribute groups or factors. Six attributes in the survey collectively measured a factor called supplier Cut-Flower Quality. These included "Freshest product available", "Highest quality cut flowers", "Provides value for the price paid", "Implements proper floral care & handling", "Best at managing the cold chain", and "Delivers via refrigerated trucks". In this reporting, we focus our discussion on these six attributes that comprise the Cut-Flower Quality factor. Also, in the following appendix, we test the validity of this six-attribute quality factor to assure readers that our model is correct.

Table 1 shows the six quality attributes and their SEM Loadings, which represent the degree of association between the attribute and the Cut-Flower Quality factor. The SEM Loadings may obtain a value between 0 (no association) and 1 (perfect association), with higher loadings representing greater association between the attribute and the factor. In general, loadings in the 0.8 to 0.9 range or higher represent "excellent" association with the factor, those in the 0.6 to 0.7 range represent "good" association, those in the 0.5 range represent "fair" association, and loadings in the 0.4 range represent "weak" association. Loadings lower than 0.4 are generally considered so weak that they are not generally reported in research findings, as these attributes have little, if any, association with a factor. For a factor, an examination of the loadings identifies the key attributes driving the overall composition of the factor.

Table 1 shows the six attributes that measure the supplier Cut-Flower Quality factor in order of their loadings from highest to lowest. The attribute, "Freshest product available" has the greatest association with the supplier Cut-Flower Quality factor (loading of 0.82). This finding indicates that as florists perceive cut-flower quality from their suppliers (wholesalers and direct sources), the perceived "freshness" of the product (e.g. time from harvest to receipt by the retailer) is the overriding top attribute driving the overall perception of cut flower quality by florists. Thus, "freshness" is a key driving attribute of the florists' "quality mind set". The freshness attribute is closely followed by an attribute that largely describes the overall meaning of the factor, "Highest quality cut flowers" with a loading of 0.80. This attribute attests to the harvest quality of the cut flower sources used by wholesalers (or quality of Miami and farm-direct sources).

The next attribute, "Provides value for the price paid" has good association with florists’ perception of supplier Cut-Flower Quality (loading of 0.74). This suggests that florists currently associate value with the supplier’s provision of Cut-Flower Quality. While this may seem intuitive, it is not always the case. In some of our marketing research projects with floral suppliers in specific markets, "value" attributes sometimes load more strongly with a Low Price factor. The alignment of value attributes with either quality or price factors is likely due to the cut-flower supply and demand conditions in the markets when they are surveyed. Since the US cut-flower market is currently considered by many as an over-supplied market (lower-priced cuts), this research reveals that florists now associate value more with the supplier’s provision of Cut-Flower Quality, rather than with the provision of Low Price. We interpret this to mean that value is now more with florists’ "quality mind set", rather than with their "price mind set".

While the above three attributes of supplier Cut-Flower Quality obtained loadings in the 0.70 to 0.80 range, meaning that florists strongly associate these attributes with supplier quality, the "care & handling" attribute, and especially the two "cold chain" attributes obtained much lower loadings. In fact, the attributes "Best at managing the cold chain" and "Delivers via refrigerated trucks" showed rather weak association, with loadings of 0.47 and 0.42, respectively, on the Cut-Flower Quality factor. These findings indicate that while florists do associate care & handling and cold chain management with supplier Cut-Flower Quality, the cold-chain association is at best, weak, suggesting that florists’ perceptions on these cold-chain attributes is hazy, incomplete, or not well understood.

These model findings indicating that retail florists perceive supplier Cut-Flower Quality primarily via freshness and value, and secondarily via provision of proper floral care & handling, and lastly via supplier cold-chain management practices may provide a dilemma for some wholesale florists. We note that the wholesaler is uniquely positioned in the floral distribution channel to maintain and manage the cold chain from floral supplier to retailer. Wholesale florists may select specific suppliers that strictly adhere to cold-chain management practices, and/or prescribe shipping modes for their own floral shipments. These wholesalers may also maintain and monitor the floral cold chain via proper refrigeration upon receipt of floral shipments, and with refrigerated truck shipment to their retail customers. Thus, by having direct communication, oversight, and influence with both floral suppliers and retailers, the wholesale florist may be in a position to best maintain and manage the cold chain throughout the distribution channel.

But here, now, is the wholesalers’ dilemma. For wholesale florists to properly leverage their investment and resource allocations for cold chain management, the cold chain must be strongly perceived and valued by retailers. The cold chain should be viewed as a key driver in obtaining and maintaining cut-flower quality. Currently, however, such is not the case, as freshness drives the Cut-Flower Quality factor, and cold-chain trails far behind.

In our view, for wholesalers to fully adopt, utilize, and derive full benefits from cold-chain management in floral distribution, retail florists must obtain a better understanding of the purpose and end-user benefits of the cold chain. Florists also should have a better understanding of their suppliers’ practices in cold-chain management. The cold chain must become an integral part of the florists’ "quality mind set". As it currently exists, it’s a far stretch at best!

After examining the SEM loadings for the supplier Cut-Flower Quality factor in Table 1, some academics and skeptics may suggest that our model of Cut-Flower Quality is incorrect, and that we are actually measuring two separate factors, supplier Cut-Flower Quality, and supplier Care & Handling (including cold-chain management). Skeptics may claim that model mis-specification between a one-factor and two-factor model could be a reason for the lower loadings for the "cold chain" attributes in our original one-factor quality model.

One major advantage of Structural Equation Modeling is that one can readily test alternative models within the SEM framework, and determine the plausibility of one model over another through several measures of overall model fit. Thus, we set forth a model test of our original one-factor model, supplier Cut-Flower Quality (Model A) against an alternative two-factor model, supplier Cut-Flower Quality and supplier Care & Handling (Model B), and determine which model shows overall superior "fit" with the florist survey data. In both models, we used SEM with maximum-likelihood estimation for our modeling analyses.

Our original one-factor model of supplier Cut-Flower Quality comprises six survey attributes as shown in Figure 1. Using the conventional SEM notation, ellipses represent factors, boxes represent measured variables, and small circles represent estimated measurement error in the measured variables. The loadings are represented as arrows emanating from the factor, as the SEM theory prescribes that the factors are manifest in the measured survey variables.

There are three popular and often used model-fit measures used to assess overall model fit for Structural Equation Models: 1) the Discrepancy Statistic divided by its degrees of freedom (df); values around 5 or less indicate a reasonable-fit model (Marsh and Hocevar, 1985), 2) the Comparative Fit Index; values close to 1.0 indicate very good model fit (Bentler, 1990), and 3) the Root Mean Square Error of Approximation, or RMSEA; values should be 0.10 or less for good-fit models (Browne and Cudeck, 1993).

Model A, our original six-attribute, one-factor model of supplier Cut-Flower Quality, reveals a fairly good model fit, as determined by all three model-fit measures. For Model A, the Discrepancy/df statistic is near 5, the Comparative Fit Index is high (close to 1.0), and the RMSEA statistic is below 0.10.

The alternative two-factor model, where three survey attributes measure supplier Cut-Flower Quality, and three survey attributes measure supplier Care & Handling (including Cold Chain) is shown in Figure 2.

The alternative model, Model B (two-factor model), reveals poor overall model fit, as determined by all three model-fit measures. While the cold chain attributes obtain somewhat higher loadings on the Care and Handling/Cold Chain factor, the Discrepancy Statistic for this model (38.8) is very far from 5, the Comparative Fit Index (0.726) is not close to 1.0, and the RMSEA statistic (0.247) is well above 0.10. The poor model fit clearly indicates that the six survey attributes do not support a two-factor structure. Thus, based on the survey data, and overall model fit, one must reject the alternative two-factor model (Model B) in favor of our original one-factor model of supplier Cut-Flower Quality (Model A). The model B findings, however, reveal that a separate factor of Care & Handling/ Cold Chain fails to emerge in our florist evaluation data, further supporting our contention that florists' perceptions of suppliers' cold chain management are hazy, or difficult for florists to assess.

Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246.

Browne, M.W. and Cudeck, R. (1993). Alternative ways of assessing model fit. In Bollen, K.A. and Long, J.S. [Eds.] Testing structural equation models. Newbury Park, California: Sage, 136-162.

Byrne, B.M. (2001). Structural Equation Modeling With AMOS, Basic Concepts, Applications, and Programming. Mahwah, NJ: Lawrence Erlbaum Associates.

Marsh, H.W. and Hocevar, D. (1985). Application of confirmatory factor analysis to the study of self-concept: First- and higher order factor models and their invariance across groups. Psychological Bulletin, 97, 562-582.

Prince, T.L. and Prince, T.A. (2006). Identifying Effective Messages for the WF&FSA Ad Campaign (Power-Point Presentation). Columbus, OH: Prince & Prince, Inc.