While researching another idea I came upon a great blog posting on twitter and its implications in the Case of Charlie Sheen.  Consider these facts.

1.  Charlie Sheen famously fought the Law and the Law won.

2.  Charlie Sheen opened a twitter account.

3.  Charlie Sheen attracted more twitter followers in a shorter period of time than anyone else in the history of . . . twitter.

4.  Charlie Sheen’s twitter account within the first hour received a Klout score of 57.

5.  Charlie Sheen did not tweet once while achieving his Web 2.0 superduperstardom.

Michelle Sullivan noted then:

What does this mean for the credibility of tools like Klout that measure online influence?  It means that they measure influence based exclusively on quantity, and not quality.  It means that they don’t take much else into account (if anything).

Sullivan then continues with a both simple and sophisticated analysis of the implications of this, generalizing past one service like Klout, and pointing to a more thoughtful consideration of measuring Web 2.0.  (Sullivan clearly thinks about persuasion with her head and not her hopes or somebody else’s hype.)

Sullivan puts a glaring spotlight on a recurring measurement and interpretation problem with the technological device in mediated persuasion and communication, especially with Web 2.0.  It’s relatively easy (though extremely expensive and time-consuming) to count noses, ears, eyes, or fingertaps with these devices, but then, what does it mean?  Even Google relies essentially on a counting system that is wildly confounded with sheer size, as if temporary popularity is a Cure for Cancer, the Second Coming, or just a really good five cent cigar.  In many instances mere counting falls into the worst effects from the Wisdom of the Crowds fable.  You never measure impact, influence, persuasion, change, baby, change; you always measure popularity in the worst sense of the term.

Back in the day of your Father’s Oldsmobile, the metric counters with TV, radio, and print had actually gotten pretty good at not only knowing How Many, but also What Effect.  In the wild west of the evolving web, counting is pretty much a mug’s game.  Yeah, Facebook has 500 million users.  And twitter generates billions of tweets weekly.

And?

Remember the Rule.

You Can Count It, But That Doesn’t Mean You Changed It.

In prior posts we looked at a Relational Marketing paper from Liu and Gal, first discussing the conceptualization and set up experiments, then key mediation and boundary experiments.  Here’s a quick recap of the paper on Relational Marketing.

Liu and Gal theorize that advice generates closeness and empathy which builds a relationship between client and organization that then produces favorable outcomes for the organization.  They establish the basic connection between advice and outcome with profit or charitable organizations and with purchase likelihood or donations.  They compare advice with two other possible client inputs, expectations and opinions, and find that advice is always superior and that expectations or opinions are often worse than a no-input control comparison.  They also test rival mediators to closeness and empathy, notably a variety of cognitive factors, and find support only for closeness and empathy.  Finally, they establish two interesting boundary conditions.  First, the advice effect works only with the organization that solicits the advice and does not generalize to other, similar organizations.  Second, incentives for providing advice, kill the advice effect.

The Liu and Gal paper illustrates four very large points which I’ll develop under these headings:  Relational Marketing,  Scientific Excellence, Bad Science, and Applied Research.  I want to underscore that these are not issues Liu and Gal raise themselves, but rather observations I’m making from reading their excellent paper.

1.  Relational Marketing

A New New Thing in marketing is the combination of social relationships with Web 2.0 to increase the effectiveness of marketing.  Cultivating and building social, rather than pure business, relationships with customers is presumed to lead to better outcomes – profit, you fools – and can be developed through web technologies like Facebook and twitter.  I cheerfully disdain this line of thinking because:  1) profitizing social relationships or socializing profitable relationships is a Titanic in search of the inevitable iceberg (gee, how about a marriage service that matches lonelyhearts with prostitutes!) and 2) novel uses of Facebook and twitter provide more benefit to Facebook and twitter than you (as the Arab street organizers discovered in 2011).  Of course, I’m an idiot who doesn’t have a Facebook or twitter account, fell in love at first sight with Melanie, and doesn’t have an iPad or iPhone.  You can’t believe a word I hoot.

Consider, then, the Gal and Liu research.  Realize the large practical difference in outcome you get depending upon whether you approach a client for advice, expectation, or opinion.  Who would have guessed that something so prosaic as asking for advice compared to asking for an opinion could produce such large differences?  Who would have guessed that advice can lead to closeness and empathy, thus building a relationship, compared to expectations?  Nowhere in the rah-rah for most Relational Marketing do you see anything this complicated, sophisticated, and, dare I say it, nuanced as this research.  Hey, just get a Facebook account, baby, and you’re doing Relational Marketing, right?

Liu and Gal prove that Relational Marketing can be effective, but you’d better do it right because if you do it wrong (expectations or opinions; paying for advice), the thing will backfire on you like a Mainway toy (YouTube).  Creating a relationship takes more than 140 characters in a tweet and requires the active participation of the client.  Gal and Liu demonstrate that Relational Marketing is less what you do than what you get the Other Guy to do.

Now, consider what happens if you follow their advice and properly employ advice with your clients.  While this research does not float downstream to the next encounter, what happens if you don’t follow the advice?  What happens when you get conflicting advice?  What happens when they see you doing this play with everyone else?  You’ve now got a business problem but not because you are doing bad business, but because you are doing bad relationship.  You cannot repair the problem with a return, a coupon, a new line of goods or services or any other standard business behavior.  You’ve got to handle the relational damage to get back to doing business.

I noted the problem of relationships with persuasion in a post about love.  I quoted from two experts, William Shakespeare and Saint Paul, about the nature of love then contrasted love with persuasion.  I’ll repeat the key point:

Persuasion disrupts, distracts, and dissolves love.  Persuasion surveils your lover to understand how best to make a change.  Persuasion puts your preferences in your lover.  And, even if the your lover benefits from this change, it is still a change you created in his head, heart, or body that he had ignored, dismissed, or resisted.  Persuasion is not love.

Now, simply substitute Relational Marketing for love and you see my concern.

Also realize the limitations of this great research.  It made no money for anyone.  All dependent variables, the outputs, were self reported through the computer and no participant was a genuine customer or client of an organization making a transaction in real time.  There is still that translation from the lab to the field or in this case the floor.  These experiments clearly demonstrate how businesses, whether profitable or charitable, can manipulate a sense of relationship, of connection between client and organization through a specific tactic, advice seeking and receiving.  It does not provide any evidence about a sales agent at Macy’s on a Tuesday morning.  You’ve got to follow my Rule, All Persuasion Is Local, to make that translation.

2.  Scientific Excellence

I point to this as one of the best research papers I’ve read.   If you’re in the theory and research business, please consider this paper as a model of clarity, organization, and intelligence.  The thing is stone cold professional.  We’ll start at the abstract then move to the hypotheses.

This research examines a novel process by which soliciting consumer input can impact subsequent purchase and engagement, namely, by changing consumers‘ subjective perception of their relationship with the organization.  We contrast different types of consumer input and propose that, relative to no input, soliciting advice tends to have an intimacy effect whereby the individual feels closer to the organization, resulting in increases in subsequent propensity to transact and engage with the organization.  On the other hand, soliciting expectations tends to have the opposite effect, distancing the individual from the organization.

And their hypotheses:

H1: Soliciting advice from a customer tends to result in greater propensity to transact with the organization, compared to when advice is not solicited.

H2: Soliciting expectations from a customer tends to result in less propensity to transact with the organization, compared to when expectations are not solicited.

H3: The change in propensity to transact due to giving advice (stating expectations) is driven at least in part by an increased (decreased) relationship closeness the customer perceives with the organization as a result of providing advice (stating expectations).

H4: The change in perceived relationship distance is due to the inherent thought process of advice-giving (stating expectations), which involves taking an empathic (self-focused) perspective towards the advice-recipient.

See how the opening of the abstract and the structure of these hypotheses reveal and explain the whole damn paper; everything works out from these statements such that the middle of the writeup is actually the beginning of the idea.  This is a great example of both excellent thinking and writing.  Gal and Liu figure it out, then express the theory in simple, direct, and clear lines.

Consider how they develop and test these hypotheses.  They employ the same basic data capture technique, that computer survey of online participant panels.  They always randomize people to controlled conditions.  They employ several different conditions to test the hypotheses.  They check that advice function in charitable and then for profit organizations.  They check advice against a no-input control (essentially the Status Quo or Standard Operating Procedure or How We Roll Around Here) and against other reasonable inputs like expectation and opinions.  They test the idea of relationship and relationship formation with a variety of mediators – their theorized mechanism of closeness and empathy against cognitive factors, for example.  Finally, they seek boundary conditions, the negative impact of incentives and the matching limitation.  Realize that any one study is useful, interesting, and confirming (and sometimes correctly disconfirming), but no single one is decisive.  Now, when you add them all together is when you see the power of this research.  It is a marvel of theory development and testing in one paper.

Of course, even this one excellent paper hardly proves decisively the Gal and Liu theorizing.  We need replications, both exact and conceptual.  We need extensions.  If this research is true, what else should be true, too?  This needs to move out farther into actual real world and real time interactions between clients and organizations.  Will advice in the field produce increased sales or donations?  What new variables will arise to surprise an entrepreneur translating this idea into practice?  But realize that all of these extensions are worth pursuing because of the quality of evidence and reasoning Liu and Gal provide.

3.  Bad Science.

Contrast the abstract and hypotheses and their structure with the kind of research I often jollystomp in this blog.  Read the rationale and conceptualization and you see the impoverished, simplistic, and assumed ideas about toxic environments, wicked advertising campaigns, greedy capitalist corporations, helpless consumers, parents, and children who are tricked into getting fat from seeing a 60 second commercial or getting a toy in their Happy Meal.  The ideas are not clearly conceptualized or well operationalized and certainly not presented in that Mediated Relationship model evident in the Liu and Gal work.  All we get is that clichéd Airing Of Grievances and Tale Of Woe as authors write up the butcher’s bill of mortality and morbidity; where’s the theory, the science, the skeptism?

And, of course, the testing is sophomoric, biased, and executed in that Ta-Da!, Look Ma No Hands style that only confirms a juvenile trick.  Just read the sources from these PB posts on failed laws regulating texting and driving, and more recently, on regulating calorie counts on menus to see the difference between good science and bad science and why All Bad Science Is Persuasive.

Liu and Gal convince me with science and need no rhetorical research or sophistical statistics in the attempt.  They know what they are doing, know how to test it, and how to describe it.  They provide a strong example for comparison with all other research or “research” you might encounter.  Look in those new papers for their deviations from the excellent model here.  There is no good reason for any researcher in any field to operate much differently from the Liu and Gal paper, yet you will clearly see that they do.  To the extent that the next paper you read misses this mark, you will probably find instead that All Bad Science Is Persuasive.

4.  Applied Research.

While you see textbook theory development and testing in the Liu and Gal paper realize that it is all in the service of applied research.  They want to do business better.  They do not invent or discover new psychological constructs in this research, but rather use the theory-research approach characteristic of scientific persuasion all to make more money.  If ever you could call research, Applied, without fear of contradiction, this paper is at the top of the list.

Thus, you can do science with practical, commonplace, prosaic, everyday, ordinary, and on and on with the thesaurus entries for applied.  So, why can’t the health, safety, and medicine guys do this, too?

It is common to read bad science in journals with content modifiers in their titles as with Health Psychology or Health Communication.  Somehow the idea that we are applying psychology or communication to a specific area of daily life seems to let researchers and reviewers off the hook for thinking, acting, and writing scientifically.  There’s that terrible Tale of Woe and all those dead or wasted bodies, shattered psyches, and on and on as if sympathy was a key element in Theory Development, Reliable and Valid Measurement, and Proper Statistical Analysis.

Of course, I see the same thing in what are supposed to be “basic” research journals.  Whenever the research is done in an applied area, science standards often disappear.  Why?  I’ve started any number of recent posts on awful studies from Psych Science that “research” political conservatism or global warming, but have to stop because it’s clear that no one is thinking like a scientist when they are working from their feelings.  What’s the point of discussing naked emperors?

Let’s get out of here . . .

This has been a long series from what appears to be just one little publication from Liu and Gal.  I hope I’ve demonstrated that there’s a lot of there, there, with the paper whether you are interested in theory or money.  This paper works that fun seam between science and practice to the benefit of each side.  It’s also a great example of great science.  Just read a few sentences in the abstract and those beautifully designed and expressed hypotheses.  Compare what you read and what you write to that standard and make your judgment.

You learn about practice, science, writing, and excellence from this paper.

We continue today our look at Liu and Gal’s work on Relational Marketing and their theorizing about advice as a customer input, closeness as a mediator, and purchase/donation behaviors as outputs.  In the prior post we learned that advice giving motivates more positive business outcomes compared to no advice or to giving expectations and that this advice giving generates a highly specific attitude toward the organization receiving the advice.  Now, while the prior two studies provided excellent supporting evidence for the beneficial impact of advice, we saw no evidence about the presumed impact of closeness.  That’s crucial for Gal and Liu’s thinking and now we’ll consider their Experiment 3 for that information.

Experiment 3 manipulated whether participants provided their advice, opinions, or expectations for an organization. Two-hundred-fifty-six (256) participants were recruited from an online pool of individuals from throughout the United States and were randomly assigned to conditions.  After reading a description of a new restaurant, Splash!, participants provided the following responses:  Purchase likelihood, subjective closeness and empathy, and perceptions of the valence of cognition, amount of help they provided, difficulty of helping, and the humility of Splash!.  Likelihood of purchase, closeness and empathy measures are the key variables from the study hypotheses, while the perception variables measure Rival Explanations.  Thus, the authors test the alternative proposition that Advice is mediated not by Closeness, but by Cognitive Effort, for example.  And, once again we compare the effect of Advice versus Expectation and the new input, Opinion.

Thus, each participant read the same description of just one organization, a for profit restaurant, then either provide Advice, Expectations, or Opinions about Splash!.  All participants then complete a battery of self report questions that measure the key outcome – purchase likelihood – and several potential mediators of that outcome, including the preferred one of closeness and empathy.  Closeness is measured through the IOS scale.  This uses visual Venn diagrams in an overlapping sequence of circles indicating No to Complete Overlap between participant and organization.  Thus, each participant indicates how close they feel to the organization with that visual portrayal.

Additionally, Gal and Liu analyze the written Advice, Expectation, and Opinion comments and code them for empathy.  Here’s how they do this.

Empathetic Perspective.  We had argued that giving advice gives rise to a subjective feeling of closeness due to the inherent demand of the task, namely, taking an empathetic perspective of the organization.  To provide evidence for this process, we had two coders, blind to the hypotheses and experimental conditions, code the content of input for the degree to which the person took an empathic perspective on a 5-point scale ranging from -2 (―restaurant‘s perspective) to 2 (―individual‘s perspective).

So, we’ve got the usual suspects of input (Advice, Expectation, and Opinion) and output (Purchase Likelihood) and two theory variables of closeness and empathy that measure the mediator.  And, just because Gal and Liu think like scientists, they also include competing mediators that could explain the input-output effect.  Please note how smart it is to include competing mediators here.  While this mediation test is only correlational, it does at least provide important comparisons to consider.  Maybe closeness is not important, but rather something related to cognition?  Liu and Gal allow for disconfirming evidence here.

Let’s break this down sequentially, starting with the purchase likelihood.

Purchase Likelihood. As hypothesized, an omnibus one-way ANOVA revealed a main effect of input solicitation mode on purchase likelihood (F(2,253) = 16.25, p < .001). Planned contrasts using one-tailed tests for directional hypotheses showed that purchase likelihood was higher in the Advice (M = 5.74) than in the Opinions condition (M = 5.05; t(157) =2.73, p < .01, d = .43), and lower in the Expectations (M =4.29) than in the Opinions condition (M = 5.05; t(171) = 2.86, p < .01, d = .44).

No surprise here, but still this is good news.  It confirms the results from the first two experiments.  Advice produces a much more positive outcome on purchase likelihood than either Expectations and Opinions.  Now, why does this effect occur?  The bet is on closeness and empathy.  Advice makes the participant feel more connected and related to the organization.

Subjective Closeness. Correspondingly, an omnibus one-way ANOVA revealed a main effect of input solicitation mode on our measure of relationship closeness, namely the IOS scale (F(2,253) = 9.81, p < .001). Planned contrasts using one-tailed tests showed that relationship closeness was greater in the Advice (M = 3.22) than in the Opinions condition (M = 2.59; t(152) = 2.31, p < .05), and that relationship closeness was lower in the Expectations than in the Opinions condition (M = 2.15; t(159) = 2.00, p < .05).

Again, the results here mirror the results with purchase likelihood.  Advice giving leads to greater feelings of closeness.  Now, this mean difference in closeness between Advice, Expectation, and Opinion is not exactly the same thing claiming closeness mediates the relationship between Advice and Purchase.  We need to run structural equation modeling on this (or path analysis or mediation analysis or regression modeling).  We will start with the simple correlation between participant input (Advice, Expectation, or Opinion) and output (Purchase).  We will then push in between the inputs and the outputs the mediators, closeness and empathy.  Let’s look at a diagram of the model, then the statistics for it.

The top model simply displays the unmediated relationship between input and output.  The more complicated model adds in the mediating path Liu and Gal theorize.  Now, consider the statistical analysis for each model.

There was a significant total effect of input on purchase (β = -.72, t = -5.71, p < .001). Further, the total direct effect (i.e., effect not mediated by the mediators in the model) was significant (β = -.34, t = -2.96, p < .01), as was the total indirect effect, with a point estimate of -.39 and a 95% confidence interval between -.56 and -.20.

Great.  The data for that first, simple model is confirming with a fairly large beta (.72) and then when Gal and Liu pull out the effect due to the mediators, that simple model is still producing a Medium size effect.  All by itself, Advice giving generates a practical, real world effect on purchase likelihood.  Now, it gets a little more complicated with the overall model.

Importantly, an examination of the specific indirect effect through both mediators indicated that the path from input mode to purchase likelihood through both mediators was significant with a point estimate for the effect of -.18 and a 95% confidence interval between -.28 and -.10. The specific indirect effect through perspective taking alone (95% confidence interval from -.17 to .04) and through relationship closeness alone (95% confidence interval from -.29 to .01) were not significant, indicating that neither was an independent mediator of the effect of input mode on purchase likelihood. In summary, when taking account of all variables in the model, the input mode à empathetic perspective à relationship closeness à purchase likelihood path through both mediators is significant, whereas the effect of input mode on purchase likelihood through either perspective-taking alone or closeness alone is not significant. This suggests indeed a multiple-step mediation has taken place.

The headline here is that the full model of input-mediator-output does a much better job at explaining the results than just the input-output model.  And, best of all, the presumed operation of closeness and empathy provide good confirming evidence as the mediators.  Advice works because it builds a relationship between the customer and the organization, stimulating feelings of closeness and a specific understanding and empathy of the customer for the specific organization.

But, of course, there could be Alternative Explanations, right?  What about a more cognitive mediation?  Maybe it’s not the relationship, stupid, but the thinking.  Liu and Gal address that reasonable concern.

Alternative Explanations. Finally, we examined measures to tap into alternative accounts of valence, helpfulness (foot in the door), and brand image.  We did not find differences along any of these dimensions.  Specifically, omnibus one-way ANOVA‘s did not show that participants varied by input form in the degree to which they focused on negative versus positive thoughts (M Advice = 4.95, M Opinions = 5.02, M Expectations = 5.01;F < 1), the degree to which they viewed their input as an act of help (M Advice = 4.51, M Opinions = 4.56, M Expectations = 4.80; F < 1), the degree to which they found providing input difficult (M Advice = 2.85, M Opinions = 2.56, M Expectations = 2.83; F < 1), or in their perceptions of Splash! as humble (M Advice = 4.69, M Opinions = 4.95, M Expectations = 4.72; F < 1) or arrogant (M Advice = 2.38, M Opinions = 2.70, M Expectations = 2.94; F(2,253) = 2.45, p = .09).

Interesting and useful null results!  All of these measures were considered as potential rival explanations as mediators of Advice-Purchase and all fail to show any systematic variation.  Given the absence of mean differences here, there’s no reason to consider the structural equation models.  Nothing is going on, especially compared to the closeness and empathy measures that indicate the relational mediation.

Now, we are closing the circle on Liu and Gal’s theory.  Once again Advice leads to very different and more positive outcomes than Expectation or Control.  Again, Expectations produce the worst outcomes even compared to doing nothing at Control.  What’s new here is the closeness and empathy variables.  They too are sensitive to that Advice function and it also fits well into a structural equation model.  That diagram shows that Advice producing Closeness and Empathy makes an important difference and that either Advice or Closeness or Empathy alone has less impact than all together.

At this point, we’ve got a great demonstration of theory construction and testing.  Best of all, the evidence confirms a particular kind of input, Advice, generates a strong sense of relationship which in turn produces a favorable business outcome.  Further, we’ve got evidence that other inputs, Expectations and Opinions, produce worse effects such that not all kinds of Relational Marketing are equal.

But, now Gal and Liu take another experimental step to test not the theory, but the practice.  Many organizations use incentives with their customers and clients.  What happens to the Liu and Gal relational theory when you pay your participants?

Experiment 4 had a 2 (Input Form: Advice vs. Opinion) × 2 (Compensation: None vs. Compensated) between subject design and was performed online.  Two-hundred-three (203) participants were recruited from an online subject pool of individuals from throughout the United States and were randomly assigned to conditions.  They read about Thai Kra,  a small Thailand-based manufacturer of seaweed snacks.  The company was interested in American consumers‘ input before possibly launching their seaweed snacks in the United States.

Participants in the Compensation conditions were informed that in return for their input Thai Kra had bought them an extra raffle entry, doubling their chances of winning an Amazon.com gift certificate.  Participants in the No Compensation conditions did not receive any information about compensation. All participants then read the same description of the company.  The key outcome was purchase likelihood.

So, we’ve got the same input, Advice, along with a comparison of Opinion.  Now, we’ve added incentive in the form of Compensation, that additional Amazon gift certificate bumped up by Thai Kra.  If you know anything about incentives, you know that they can have perverse effects.  Instead of obtaining that common sense hydraulic of Bigger Reward makes Bigger Effects, you can actually kill desired outcomes.  What happens here with relationships?

A planned contrast using a one-tailed test  showed that among participants that were not compensated for their input, there was a greater likelihood of trying Thai Kra‘s seaweed snacks when they provided advice (M = 4.81) than when they provided opinions (M = 3.50; F(1, 100) = 10.83, p < .01, d = .66), consistent with all previous experiments.  In contrast, among participants that were compensated for their input, there was a similar likelihood of trial regardless of whether they provided advice (M = 3.89) or opinions (M = 4.00; F < 1).

Interesting, isn’t it?  We’ve shown that Advice produces feelings of closeness and empathy which builds the relationship which then makes positive outcomes like purchase or donations more likely.  Relational Marketing can work!  But, now when organizations provide incentives as part of the relationship, bang, you kill the Advice effect.  Think about that.

Let’s put this paper back together again.

Liu and Gal theorize that advice generates closeness and empathy which builds a relationship between client and organization that produces favorable outcomes for the organization.  They established the basic connection between advice and outcome with profit and charitable organizations and with both purchase likelihood and donations.  They compared advice with two other possible client inputs, expectations and opinions, and found that advice was always superior and that expectations or opinions were often worse than a no-input control comparison.  They also tested rival mediators to closeness and empathy, notably a variety of cognitive factors, and found support only for closeness and empathy.  Finally, they established two interesting boundary conditions.  First, the advice effect works only with the organization that solicits the advice and does not generalize to other, similar organizations.  Second, incentives for providing advice, kill the advice effect.

Please mull over this post and the prior one.  There are many implications to this paper and I’ll detail my observations in tomorrow’s third and final post.

I’ve found a great paper that illustrates several large ideas within its excellence. Liu and Gal demonstrate the power of good science for understanding new ideas and in so doing also highlight the many failures bad science stimulates. From one somewhat simple report on Relational Marketing, I’ll develop a series of ideas and will need three posts to handle it. Today we’ll look at the first two experiments from Liu and Gal; tomorrow we’ll explore the last two experiments from their report; and the day after that we’ll pull back for a wider view of this research and its many lusters.

Liu and Gal investigate what I’ll call Relational Marketing in their JCR paper, Bringing Us Together or Driving Us Apart: The Effect of Soliciting Consumer Input on Consumers‘ Propensity to Transact with an Organization. I don’t know Liu and Gal from Adam, Eve, or the serpent, but this is excellent work and I recommend a close reading of it to anyone interested specifically in Relational Marketing, persuasion in general, excellent scientific writing, or how to think well.

This research examines a novel process by which soliciting consumer input can impact subsequent purchase and engagement, namely, by changing consumers‘ subjective perception of their relationship with the organization. We contrast different types of consumer input and propose that, relative to no input, soliciting advice tends to have an intimacy effect whereby the individual feels closer to the organization, resulting in increases in subsequent propensity to transact and engage with the organization. On the other hand, soliciting expectations tends to have the opposite effect, distancing the individual from the organization.

Observe immediately that this research aims at testing the impact of socializing profitable relationships through concepts of advice and expectations, of intimacy and closeness. Underline that it deconstructs the presumed simple main effect of Web 2.0 (social media for socializing profit) into different parts and suggests some kinds of social relating may function differently from others, which is not exactly that cheerleading Facebook4Profit!!! exhortation.

Gal and Liu argue that the relationship between a customer and a business must generate feelings of closeness and intimacy. Thus, no matter how a business engages customer input, if this does not produce closeness, then the relationship will not produce a positive business outcome.

Now. How do you generate this closeness? Liu and Gal start with advice. They advise that when a business asks a customer for advice, this must make the customer look at the business in a more relational way especially compared to offering expectations (what I expect you to do) or attitudes (how I evaluate what you do) about the business.

They then test various customer inputs (provide advice, expectations, or opinions), the presumed mediators (intimacy and closeness), under various conditions (profit or nonprofit corporations; paid or volunteer; donation types), with different measures (simple self report; virtual cash contributions) in 4 lab experiments.

All experiments employ a cover story that the study authors are working with organizations (profit or charitable) to help them with their missions. Participants are from samples of online volunteers who are then randomly assigned to one condition that manipulates hypothesized variables. They read information about that organization at a computer station and respond to all elements through the keyboard. Finally, participants are rewarded with a raffle entry for gift certificates. Let’s detail each study.

Experiment 1 had a 2 (Input Form: Advice vs. Control) × 2 (Input Recipient: Building Hope vs. Preemie Promise) × 2 (Donation Recipient: Building Hope vs. Preemie Promise) between subject design and was conducted online. Three-hundred-fifty-two (352) participants were recruited from an online pool of individuals from throughout the United States and were randomly assigned to conditions. People read descriptions of the organization (Building Hope for domestic violence victims or Preemie Promise for premature infant care).

In Control, participants just read the description, while in Advice, after reading the participants were asked: We are interested in what advice you might have for our organization? After entering any advice they had, participants were thanked.

Now, after this, all participants were given a chance to read the description of the other organization. They were then offered an opportunity to donate any raffle winnings to either the first organization they’d read about or the second one. This tested whether people were sensitive to that Advice manipulation or whether they were just generally more charitable.

Here an example of an organization description.

Building Hope

Building Hope was founded in 2007 by Elana Lee, a former battered woman, to help aid other battered women and children.

Family violence is the number one crime and cause of injury to women in the U.S. and is believed to be the most common, yet least reported crime in the country. Building Hope aspires to become a model for the nation by introducing innovative programs at shelters designed to help women and children become more self-sufficient.

Building Hope has established a team of dedicated volunteers that give generously of their time and resources to make a difference in the lives of women and children who have been victims of domestic violence.

Got it? You read about an organization, provide your advice, read about another organization, then can make a donation to just one of them. We then analyze the donations in a 3 way ANOVA of Input (Advice or Control), Advice Organization (Hope or Promise), and Donation Organization (Hope or Promise). Here’s a graphic of the outcomes.

Making no decision based on graphic data, we look at the tests. And, indeed, the triple interaction is significant: (F(1,344) = 6.72, p = .01). Bang. Sampling variability is not a plausible rival explanation here since these results are well outside of random variation that could arise from merely randomizing to conditions. This triple is important because it fits the theory Gal and Liu believe and quite specifically. Now, we can look at specific directional hypotheses, right?

Focusing on those giving input to Preemie Promise, consistent with H1, participants giving advice to Preemie Promise donated more to Preemie Promise (M = $3.52 than participants who merely read about Preemie Promise (M = $2.55; F(1,89) = 4.38, p < .05, d = .44). In contrast, consistent with H1a, participants that gave advice to Preemie Promise did not differ in the amount they donated to Building Hope (M = $2.41) from participants who merely read about Preemie Promise (M = $2.79; F < 1).

So, with the specific organization, Preemie Promise, we get that predicted specific donation effect. Participants contribute to a charity that solicited their advice, but not a different charity. It looks like advice is necessary. Now, what about the donations for Building Hope?

A similar pattern was observed among participants providing input to Building Hope. In particular, consistent with H1, participants giving advice to Building Hope donated more to Building Hope (M = $3.69) than those that merely read about Building Hope (M = $2.53; F(1,88) = 6.34, p < .01, d = .53). However, consistent with H1a, participants who gave advice to Building Hope did not differ in the amount they donated to Preemie Promise (M = $2.55) from those who merely read about Building Hope (M = $2.52; F < 1).

Thus, asking for Advice motivated more donations, but only for the organization that solicited the Advice. Control people who never provided advice gave equally to both organizations. Advice people, however, gave only to advice seeking organizations. So, advice motivates a better outcome – the donation. And see that the effect sizes are that Medium Windowpane, 35/65 range.

Experiment 1 provides confirming evidence that Advice, compared to a no request Control, generates a better outcome in the form of greater donations. Further, we see that this effect is specific to the organization that makes the request for Advice and does not stimulate a general motivation to help a similar organization. This looks like a relationship effect whereby the Advice giving makes the participant feel more connected specifically to the source who requested and received the Advice.

From a General ELM perspective, Advice appears to function as a WATTage switch that causes participants to think about the organization more carefully and effortfully and especially along relational lines. In some respects Advice is like Forewarning, Role Playing, or even Cognitive Tuning. It activates a thoughtful response from the participant and lets each person discover their own Arguments, then permits them to express those Arguments when they type in the Advice. They really think about this and take the Central Route.

Further supportive, but not conclusive, evidence of this high WATT processing is found in that differential donation outcome. People donate to the Advice requesting organization, but not a similar organization. That’s a fairly particular, unique, and specific response and characteristic of a Central Route attitude. You could also train a Cued response to be this specific, but I’d expect that to take several trials rather than this one shot performance. Furthermore, you’d need some kind of trigger related to the Cue when you made the donation request.

So, we’ve got a good start here on understanding Relational Marketing. One tactic, Advice, appears to generate better outcomes, shows a nice interaction with donation source (Same versus Different), and fits a good theory in the General ELM. Now, would this effect generalize from a charitable organization to a commercial, for-profit organization? And, more interestingly, what would happen if we solicited a different kind of input from the participant, say Customer Expectations?

Experiment 2 has 3 conditions (advice, expectations, and no-input). One hundred thirty-one (131) participants were recruited from an online subject pool of individuals from throughout the United States and were randomly assigned to conditions. They all read a description of a for profit business EcoGym and were then asked to give Advice, Expectations, or in Control just read the description. Everyone then self reported likelihood of purchasing a membership for this business, described as:

EcoGym is a new “green” concept in fitness clubs. Our goal is to develop an ecologically friendly gym from the ground up. We intend to reduce our energy consumption by building our gym to allow in natural lighting and by using high quality insulation materials to reduce energy consumption from heating and cooling. The materials we intend to use to decorate the gym will include natural woods and fibers. Moreover, the gym will incorporate solar panels for energy generation and fitness equipment, such as treadmills and exercise bikes, will convert members’ exercise power into electric power to operate the gym. The gym will also include a cafe featuring all-natural and organic products, such as healthy smoothies and energy bars.

Hey, The Lean Green Machine!

Now, the data.

An omnibus one-way ANOVA revealed a main effect of input form on purchase likelihood (F(2, 128) = 7.62, p < .001). Planned contrasts using one-tailed tests for directional hypotheses showed that participants in the advice condition expressed a greater likelihood of purchase (M = 4.29) than participants in the control condition (M = 3.60; t(84) = 1.78, p < .05, d = .38), who in turn expressed a greater likelihood of purchase than participants in the expectations condition (M = 2.77; t(87) = 2.20, p < .05, d = .47).

Once again, Advice produces the better outcome, this time for a commercial rather than charitable organization. And the effect is that Medium Windowpane, 35/65. Notice, too, that Expectations produce the lowest likelihood of purchase even compared to the mere reading Control condition. Clearly there is a large practical difference between Advice and Expectations for the consumer. While Liu and Gal do not report it, the difference between Advice (M = 4.29) and Expectation (M = 2.77) must be a near Large Windowpane effect, 25/75.

Up to now Liu and Gal have demonstrated that a customer input, Advice, produces better outcomes for both a charitable and a commercial organization on both self report (purchase likelihood) and donation (raffle winnings). They’ve also demonstrated that Advice does not appear to stimulate a general positive attitude, but rather a specific positive attitude that is unique to the requesting organization. This interaction is crucial to Gal and Liu’s theory about Relational Marketing. Advice giving appears to generate a connection between organization and client that is different from Expectations, for example.

Now, many researchers would quit here having delivered such positive and confirming data, but Liu and Gal take an important next step. They want to test and document the impact of closeness and intimacy. They argue that perceptions of closeness mediated the effect of advice, that in essence, advice giving and receiving generates feelings of closeness in the advice-giver and that drives outcomes like purchase likelihood or donations. In the next post we’ll look at how Gal and Liu test this along with a fourth study that provides an interesting boundary condition.

Our world means computers, physical devices that perform binary operations at the speed of electricity and someday maybe at the speed of biological cells.  In the beginning, computers meant Computing.  After the Fall, it means Computering.

The mere addition of an er, the er as a vocal hesitation combined especially with “like,” the er of Ur, the primitive, the basic, the earth, changes WATT from Thinking for Computing to Tapping for Computering.

er . . . low WATT.

er . . . iGizmo.

er . . . WATtap.

er . . . like.