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The International Journal of Logistics Management
Fast or free shipping options in online and Omni-channel retail? The mediating
role of uncertainty on satisfaction and purchase intentions
Siqi Ma,
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Siqi Ma, (2017) "Fast or free shipping options in online and Omni-channel retail? The mediating
role of uncertainty on satisfaction and purchase intentions", The International Journal of Logistics
Management, Vol. 28 Issue: 4, pp.1099-1122, https://doi.org/10.1108/IJLM-05-2016-0130
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Fast or free shipping options in
online and Omni-channel retail?
The mediating role of uncertainty
on satisfaction and purchase
intentions
Siqi Ma
University of Arkansas, Fayetteville, Arkansas, USA and
University of Akron, Akron, Ohio, USA
Abstract
Purpose –The purpose of this paper is to investigate the effect of a dimension of logistics service quality
(delivery time) interacting with shipping charges and purchase importance on customer satisfaction and
purchase intentions in an e-commerce context. Uncertainty in terms of perceived ambiguity and perceived
riskiness is shown to be the theoretical mechanism that plays a mediating role in the relationships between
delivery time and customer satisfaction, as well as between delivery time and purchase intentions.
Design/methodology/approach –This study used a scenario-based role playing experiment. Three
variables are manipulated in the design of the study–delivery time, shipping charges, and purchase importance.
Participant responses (n¼360) were collected through Amazon Mechanical Turk with perceptual measures.
Findings –Results indicated that increased delivery time significantly increased customers’perceived
ambiguity and perceived riskiness which reduced satisfaction as well as negatively impacted purchase
intentions. Further, free shipping reduced customers’perceived ambiguity when delivery time was lengthy,
but strengthened the perception of ambiguity when the delivery time was short.
Originality/value –This paper sheds light on how a dimension of logistics service quality (delivery time)
interacts with shipping charges and purchase importance to impact customer satisfaction and purchase
intentions. It introduces uncertainty in the form of perceived ambiguity and perceived riskiness, to the
logistics service literature as the mechanism that can explain how delivery time interacting with shipping
charges and purchase importance impact customer satisfaction and purchase intentions. The implications for
online retailers are that they should display separate shipping charges for shorter delivery times but for
longer delivery times they should display a total price for the product which includes the shipping cost. Also
when the purchase is important to the customer, they should offer shorter shipping time choices if they want
to increase customer satisfaction.
Keywords E-commerce, Delivery time, Perceived ambiguity, Perceived riskiness, Purchase importance,
Shipping charges
Paper type Research paper
Introduction
E-commerce has seen tremendous growth in sales in recent years. The US e-commerce
market has increased from $31 billion in 2001 to $166 billion in 2008, and to $305 billion in
2014 (Rao et al., 2011; Griffis et al., 2012). Both pure online retailers as well as Omni-channel
retailers are designing and redesigning their distribution network infrastructure to promote
and to cope with this growth (Bell et al., 2014). In making informed decisions about these
networks, picking nodes, selecting pricing strategies and service levels, one of the
determining factors that promote the success of online retail is logistics service quality.
Excellence in logistics operations has become a crucial aspect
to maintain and develop the competitive advantage for online retailers in the
hyper-competitive business environment (Yazdanparast et al., 2010). To ensure the
success of e-commerce, online retailers must understand the needs of customers in term of
logistics services in order to provide them the highest value to meet their expectation and
The International Journal of
Logistics Management
Vol. 28 No. 4, 2017
pp. 1099-1122
© Emerald Publishing Limited
0957-4093
DOI 10.1108/IJLM-05-2016-0130
Received 5 June 2016
Revised 22 September 2016
4 November 2016
Accepted 12 November 2016
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0957-4093.htm
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create satisfaction (Ricker and Kalakota, 1999). Since product delivers directly to a
customer’s home or business rather than to the store in online shopping, the quality of
logistics services concerning the timeliness of “last mile”delivery is a significant
determinant of customer satisfaction and purchase intentions (Lee and Whang, 2001).
Collier and Bienstock (2006) suggest that timeliness of delivery as one of the dimension of
e-service quality has a significant influence on customer satisfaction and future behavioral
intentions. Rao et al. (2011) indicate that physical distribution service quality is a key
indicator of customer’s purchase satisfaction which impacts customer retention.
While this body of work on logistics service quality has shed some light on customer
satisfaction, the literature has not yet investigated the impact of each dimension of logistics
service quality on customer experiences such as satisfaction and purchase intentions.
Since product delivery issues become more salient to customers (Esper et al., 2003), the
objective of this study is to investigate the impact of delivery time –one dimension of
logistics service quality –on customer satisfaction and purchase intentions. Further,
marketing literature indicates customer’s satisfaction and purchase intentions can be
affected by uncertainty (Crosby and Stephens, 1987). This paper proposes that perceived
ambiguity and perceived riskiness –two dimensions of uncertainty –are the mechanisms
that can explain why delivery time impacts customer’s satisfaction and purchase intentions.
Customers increasingly want better physical distribution service quality, especially a
shorter delivery time. Many customers view themselves as “time starved”and the lengthy
delivery time makes customers anxious and dissatisfied with the purchase (Collier and
Bienstock, 2006). Online retailers are learning to provide alternatives to compensate for the
lengthy delivery time by offering free shipping. For example, Nordstrom offers free
shipping for online purchases with three to six business day delivery; their two-day delivery
charges $15, next day delivery is $25, and Saturday delivery is $35. Therefore, shipping
charges act as an alternative factor to delivery time that can impact customers’behaviors.
Lewis (2006) presents a study of the effects of shipping charges on customer acquisition,
retention, and order size. Results indicate that shipping charges significantly impact both
order incidence and order size. Gümüşet al. (2013) argue that shipping and handling
surcharge, one of the main complaints about online retailing, has a significant negative
effect on customer purchase decisions. Further, customer behaviors can be influenced by
purchase importance. Ostrom and Iacobucci (1995) find that customers weigh purchase
criticality differently. They place greater importance on price under conditions for less
critical services and greater importance on quality for more critical services. Therefore,
shipping charges and purchase importance are potential moderators that may strengthen
the relationship between delivery time and perceived ambiguity and perceived riskiness.
This study will provide two key contributions to the literature. While prior research has
studied the effect of logistics service quality on customer’s experience, they have not
attempted to explore the influence of each dimension of logistics service quality. Specifically,
this study explores the impact of delivery time, one dimension of logistics service quality,
which interacts with shipping charges and purchase importance on customer satisfaction
and purchase intentions. In addition, this study is trying to understand the underlying
mechanism that explains how delivery time influences satisfaction and purchase intentions.
Perceived ambiguity and perceived riskiness are proposed to be the dimensions of
uncertainty that lead to this influence.
This paper is organized in the following manner. A review of literature related to
delivery time, shipping charges, and perceived uncertainty is initially provided, followed
by hypotheses development and the conceptual model. An overview of the methodology
used and results from the scenario-based role playing experiment is next offered.
Finally, a discussion and implications for online retailers are offered along with
suggestions for future research.
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Literature review
This paper builds on a growing scholarly interest in logistics service quality in an e-commerce
environment. Most retailers are moving toward either Omni-channel or pure online
formats –the trend is toward diminishing pure brick-and-mortar retailers (Verhoef et al., 2015).
Prior research in this stream investigates how logistics service quality impacts customer online
shopping experiences, loyalty, and satisfaction (e.g. Esper et al., 2003; Rao et al., 2011;
Griffis et al., 2012). Most of the studies in this area have been done at the aggregate level which
examine logistics service quality as one concept. However, customers may value different
dimensions of logistics services quality such as order quality, information quality, order
accuracy, or timeliness (Mentzer et al., 2001) differently. In particular, in the e-commerce
shopping environment, customers are more sensitive to delivery time (Keeney, 1999).
Therefore, this research investigates the issue at a more granular level to focus on the impact of
one dimension of logistics service quality –delivery time, on customer’ssatisfactionand
purchase intentions.
Shipping options
Delivery time. Traditionally, “store location”has been viewed as the most important factor in
terms of profitability in brick-and-mortar retailing (Esper et al., 2003). Good store locations
allow easy access, attract large numbers of customers, and increase the potential sales of
retail outlets (Turhan et al., 2013). However, e-commerce has revolutionized the way retailers
operate. When approaching retailing from an online perspective, the importance of location
is reduced or eliminated, and distribution efficiency becomes one of the most crucial factors
that determines retailers’competitive advantage (Alba et al., 1997; Esper et al., 2003).
The operations management literature has explored physical distribution service in an
e-commerce context. Rabinovich and Bailey (2004) assess the impact of factors such as
pricing, transaction attributes, and firm attributes on physical distribution service quality
which is measured by availability, timeless, and reliability. They find that physical
distribution service quality has a significant impact on both firm profitability and customer
satisfaction in an e-commerce context. Vickery et al. (2003) operationalize customer service
in automobile industry as delivery speed and product support. Results suggest that
customer service can improve firm’s financial performance. Further, Vaidyanathan and
Devaraj (2008) find that compared to fulfilled order accuracy, timeliness of fulfilled order has
a more significant impact on satisfaction. Similarly, Rao et al. (2011) provide evidence that
physical distribution quality and physical distribution price are positively related to
customer’s purchase satisfaction and customer retention. Innis and LaLonde (1994) suggest
that according to customers’ratings, some of the physical distribution service components
such as projected shipping date or projected delivery date are the most important
information that a firm has to offer to ensure success.
However, there is limited literature focusing on the underlying dimensions of physical
distribution service –availability, timeliness, and quality of the product delivered.
Mentzer et al. (1989) expressed the importance of understanding how the underlying
dimensions of physical distribution service should be integrated into the overall customer
service package to best meet customer expectations and needs. Bienstock et al. (1996) call for
further inquiries into assessing the relative importance of the three dimensions of physical
distribution service quality across different industries. Therefore, this paper investigates the
effect of delivery time which capture the timeliness of logistics service quality in an
e-commerce context on customer satisfaction and purchase intentions.
Shipping charges. Different from traditional brick-and-mortal customers, customers who
make a purchase online have the convenience that they can receive the product without
leaving their home. In some circumstance, they receive free shipping, but sometimes,
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they have to pay for the service. The combinations of delivery times and shipping charges
are called shipping options (Smith and Brynjolfsson, 2001; Li and Dinlersoz, 2012).
From a theoretical perspective, retailers can charge a total price of a product that
separates out into two or more mandatory components such as the base price charged for
the product and a mandatory surcharge for shipping (Hamilton and Srivastava, 2008).
Retailers can also implement a combined price that includes the price of the product and the
shipping fee (Lee et al., 2014).
A considerable amount of previous research has examined the issue of shipping charges
on customerbehaviors. Various customersview shipping charges differently. Some customers
consider shipping charges negatively such that they treat it as an unfair source of profit for an
online retailer (Schindler et al., 2005). Therefore, they are more sensitive to the shipping
charges and prefer free shipping. On the contrary, other customers may consider shipping
charges as a fair charge to cover the cost of product delivery (Schindler et al., 2005), so they are
less sensitive to shipping charges and are willing to pay for shipping. Smith and Brynjolfsson
(2001) suggest that customers are almost twice as sensitive to changes in the shipping fee as
they are to changes in the price of the product. Hamilton and Srivastava (2008) argue that
customers are more sensitive to the components of total price which provides relatively low
consumption benefits such as shipping charges and less sensitive to the components of the
total price which provides relatively high consumption benefits such as product price. Further,
Lewis (2006) provides evidence that free shipping, the combined price which includes both
shipping charges and product price in total price, can change customers’ordering behaviors
such that customers order more frequently, but they order less each time. Morwitz et al. (1998)
show that shipping charges can reduce the perceived total product cost and online retailers
can use partitioned prices to increase their profitability.
Free shipping is viewed an alternative to lengthy delivery time that can maintain
customer satisfaction. Therefore, this study is trying to understand how the interaction of
delivery time and shipping charges influence customer satisfaction and purchase intentions.
In addition, prior studies have shown the direct link between logistics service quality and
customer behaviors without understanding the mechanism of how logistics service quality
has an effect on customer behaviors. Pavlou et al. (2006) have pointed out that uncertainty is
one of the factors involved in the online purchasing process that can impact customer
experience. We propose that uncertainty with the specific dimensions of perceived
ambiguity and perceived riskiness is the mechanism that may explain the relationship.
Perceived ambiguity and perceived riskiness
In the case of an online purchase, customers cannot receive the product immediately and
have to wait until the product is delivered. Therefore, they may perceive uncertainty during
the product delivery process. Perceived uncertainty becomes one of the important factors
that determines customer choice (Liang and Huang, 1998). Uncertainty, in this paper, refers
to the cost associated with an unexpected outcome and asymmetry of information
(Liang and Huang, 1998). It includes two dimensions: perceived ambiguity and perceived
riskiness (Venkatraman et al., 2006).
Perceived ambiguity is defined as individuals’perception that probabilities of possible
outcomes are vague due to missing information (Venkatraman et al., 2006). It is the subjective
experience of missing information relevant to a prediction (Baron and Frisch, 1994). Perceived
riskiness is defined as an individuals’anticipatory appraisal of his or her vulnerability to a loss
whichcanbeinterpretedasanundesirable outcome (Venkatraman et al.,2006).Assuch,perceived
riskiness is the subjective expectation of a loss or undesirable outcome (Sweeney et al., 1999).
There is much evidence that ambiguity and risk are distinct dimensions of uncertainty.
First, individuals behave differently when they face ambiguous and risky situations.
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In Ellsberg’s (1961) experiment, subjects confronted two urns containing red and black
balls, from one of which a ball would be drawn at random. Subjects were asked to bet on a
color (either red or black), then they decided to draw a ball from one of the urns. If what they
bet matched with what they drew, they would receive $100, otherwise $0. They had the
following information. Urn I contained 100 red and black balls, but the ratio of black and red
balls are unknown to the subjects; In Urn II, it contained 50 red balls and 50 black balls.
Results show that people avoid ambiguous gambles (which feature vague or imprecise
probabilities) and favor gambles with unambiguous or known probabilities. Second, they
use different language to describe ambiguity and risk. People use words such as confident,
sure, or certain to describe ambiguous situations –implying that in this case the
uncertainty is an awareness that they may not have all the information they might need
(Ulkümen et al., 2015). They use the terms likely, chance, or probability to refer to risky
situations –implying that uncertainty is due to stochastic variability (Ulkümen et al., 2015).
Finally, individuals’perceptions of ambiguity and risk have been shown to be
psychometrically distinct constructs in the literature (Venkatraman et al., 2006). Perceived
ambiguity emphasizes the awareness of relevant information that is unknown, but could be
known (Fox et al., 2011). Perceived riskiness focuses on the exposure to harm or loss
(Venkatraman et al., 2006) in making a decision.
Hypotheses development
The effect of delivery time
Delivery time in this paper includes two components: actual shipping time and acquisition
time, the time it takes a retailer to get the product out of the warehouse before it can be
shipped (Brynjolfsson et al., 2004). Customers derive utilitarian value from efficient and
timely service delivery in general (Childers et al., 2002). Time, in the e-commerce shopping
environment, represents the customer’s most precious and least replaceable asset
(Kleijnen et al., 2007). Heim and Sinha (2001) find that customers make an online purchase
for three reasons: convenience, time, and delivery. If delivery time is lengthy, the perception
of convenience may be diminished, and their utility function may decrease with increasing
delivery time. Consequently customer satisfaction may also be diminished and their future
willingness to purchase may be eroded. Hence, this paper hypothesizes that with increased
delivery time, the customer satisfaction and purchase intentions are decreasing:
H1a. There is a negative relationship between delivery time and customer satisfaction
with the product.
H1b. There is a negative relationship between delivery time and customer’s purchase
intentions.
When customers purchase the product online, there is information asymmetry between
online retailers and customers (Liang and Huang, 1998). Customers lack information to
predict when the product will be processed by the retailer and how long it will take to deliver
the product. In addition, Holloway and Beatty (2008) report that delivery problems are the
most frequently stated problem with online purchase: 64.2 percent unsatisfying incidents
were coming from fulfillment failure. Since customers are not physically at virtual stores,
the recognition of the delivery problem might be delayed. With longer delivery time, it is
plausible that the delivery process may seem to have more components or appear to be
shipping from a greater distance, and it is easy to visualize that some unforeseen event may
cause a problem in the order fulfillment process. Therefore, we argue that customers are
more likely to have the feeling that incidents may occur during the shipping period such
that the product they ordered online may not be processed in a timely manner by the
merchant, or that there will be a shipping problem en route. Customers perceive that they
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lack information to predict whether the problem will occur that may delay the delivery.
Therefore, this paper hypothesizes that increased delivery time can increase customer
perceptions of ambiguity:
H1c. There is a positive relationship between delivery time and perceived ambiguity.
Further, when the delivery time is lengthy, using similar reasoning as above, customers may
also perceive a greater likelihood that something may go wrong with their order. If something
goes wrong and their order is delayed, they anticipate a greater likelihood of this undesirable
outcome. Therefore, customers perceive higher risk. Thus, this paper hypothesizes that
customers’perception of risk is positively affected by increased delivery time.
H1d. There is a positive relationship between delivery time and perceived riskiness.
The moderating role of shipping charges
To a rational customer, free shipping or shipping fees should not matter, because their purchase
decisions ought to be based on total price (Gümüşet al., 2013). As long as the total price is the
same, customers should not exhibit different shopping behaviors. However, the behavioral
marketing literature indicates that customers do not weigh product price and surcharges such as
shipping charges equally. This brings the question “Are customers more willing to obtain free
shipping?”In the context of a free offer promotion along with a purchase, several studies find that
free is not always good. Chandran and Morwitz (2006) suggest that free shipping has a greater
impactontheevaluationofanofferthanadiscount of equal value because a free promotion
would distract people from other considerations. Similarly, Kamins et al. (2009) argue that
customers are willing to pay a lower price for a stand-alone focal product when a supplementary
product is described as free, compared to the situation when there is no mention of free in a
promotion. Further, Palmeira and Srivastava (2013) provide evidence that customers evaluating a
promotion with a free offer have the expectation that the product being offered for free would be
oflowcostinordertobegivenawayforfree.There is also evidence that the focal product is of
low quality necessitating the promotion with a free product (Raghubir, 2004; Kamins et al., 2009).
Therefore, the collective weight of the previous research described above suggests that
free offers may devalue logistics service perception by customers (Raghubir, 2004;
Kamins et al., 2009). In the e-commerce context, when customers obtain free shipping, they
may expect that their delivery may not be guaranteed because they receive a free service
rather than pay for it. Thus, the effect of longer shipping times on ambiguity may be
amplified when shipping is free as opposed to when shipping is paid:
H2a. The positive effect of delivery time on perceived ambiguity will be moderated by
shipping charges, such that shipping charges that are seen as free will strengthen
the effect on perceived ambiguity.
Customers who purchase from online retailers are frequently quite sensitive to product delivery
issues. Online retailers are learning how to offer alternatives to short delivery schedules by
offering free shipping for a certain minimum purchase amount. These offers of free shipping are
usuallyaccompaniedbyanextendeddelivery date, such as one to two weeks (Collier and
Bienstock, 2006). With the extended delivery date, customers by strength of association with
their previous experiences may perceive a higher likelihood of the undesirable event that they
will not receive the product on time (Koukova et al., 2012). Thus, they will perceive higher risk
with the free shipping promotion. Therefore, the current research proposes that when shipping
is free, the effect of increased delivery time on perceived risk may be amplified:
H2b. The positive effect of delivery time on perceived riskiness will be moderated by
shipping charges, such that shipping charges that are seen as free will strengthen
the effect on perceived riskiness.
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If the delivery time is lengthy, customers have to wait for the product and their utility of the
product will be reduced. If they receive free shipping, they may derive the expectation that
the delivery time will be lengthy and that the retailer offers free shipping to compensate for
the waiting. Xia and Monroe (2004) find that paid shipping can enhance customer’s
purchase intentions, price satisfaction, and perceptions of product value. This indicates that
free shipping can erode customer satisfaction and purchase intentions. Therefore, this paper
hypothesizes that free shipping can strengthen the negative relationship between delivery
time and satisfaction and delivery time and purchase intentions:
H2c. The negative effect of delivery time on satisfaction will be moderated by shipping
charges, such that free shipping will strengthen the negative effect on satisfaction.
H2d. The negative effect of delivery time on purchase intentions will be moderated by
shipping charges, such that free shipping will strengthen the negative effect on
purchase intentions.
The moderating role of purchase importance
In the marketing literature, purchase importance is a crucial factor that determines customer
satisfaction (Belonax et al., 2007; Tam, 2011). It is defined as customers’perceptions of
significant importance of a particular purchase to the customers’objectives (Hutt and
Speh, 2001; Tam, 2011). Howard and Sheth (1969, p. 28) argue that purchase importance is
“the relative intensity of motives that govern buyer’s activities relating to the given product
class relative to other product classes.”The strength of the motive stems from the
importance of the purchase to customers. For example, the motive may be stronger for
customers who want to find an anniversary gift within the next two hours than customers
who want to buy a gift for his/her friend. The motive to purchase the product on time in the
former situation is higher because this purchase is more important to the customer.
When customers consider the purchase to be very important, they tend to engage in more
information search activities and exert greater effort in evaluating available retailers
(Bloch and Richins, 1983). Customers may form high expectations for the positive outcome
of the purchase such as quick delivery. For example, a customer purchasing an anniversary
gift online expects that the gift will arrive on time, the quality of the gift will be high, and the
gift will be delivered in a good condition (e.g. no tears on the box or in the clean condition).
In contrast, if the product is not important, customers may be less concerned about the
outcome such that if the product has a delayed delivery, they will not be frustrated and
anxious. The emotional state of arousal due to purchase importance may cause them to
focus attention on possible unforeseen (and thus unknown) causes of a delay (Yiend, 2010),
thus amplifying the effect of increased delivery time on perceived ambiguity:
H3a. The positive effect of delivery time on perceived ambiguity will be moderated by
purchase importance, such that an important purchase will strengthen the effect on
perceived ambiguity.
Using a similar argument as above, when customers attach more importance to the
purchase, they are more anxious, and they are more likely to visualize possible causes of a
delay (Yiend, 2010). This evaluation of more causes may result in an evaluation of increased
likelihood of events that will result in the undesirable event of a delivery delay (Kahneman
and Tversky, 1972). They do not intend to have any delays because they would suffer from
a big loss if the product is not delivered on time. This leads to the following hypotheses:
H3b. The positive effect of delivery time on perceived risk will be moderated by
purchase importance, such that an important purchase will strengthen the effect on
perceived riskiness.
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When the purchase is more important to the customer, they may want to get it as quickly as
possible to ensure that they have the product on time with flawless service. However, with
the longer delivery time, there may be an enhanced salience of possible delays.
Therefore when the purchase is more important, the effect of longer delivery times may be
enhanced. Hoffman and Kelley (2000) propose that if the purchase is important, customers
are more likely to take action to remedy an undesirable possibility. Therefore, customers
may be less willing to purchase the product and less satisfied with the product if delivery
time is lengthy:
H3c. The negative effect of delivery time on satisfaction will be moderated by purchase
importance, such that an important purchase will strengthen the effect of delivery
time on satisfaction.
H3d. The negative effect of delivery time on purchase intentions will be moderated by
purchase importance, such that such that an important purchase will strengthen
the effect of delivery time on purchase intentions.
The effect of uncertainty on satisfaction of the product
Customer ambivalence and emotion elicitation research holds that paradoxical consumption
experiences generate anxiety and stress that elicit coping responses from customers (Yi and
Baumgartner, 2004). When customers make the purchase online, they are unable to
determine with a reasonable degree of confidence whether the product is in a good quality
and whether they will receive the order on time and with a good condition due to lack of
information. This makes customers’evaluation difficult and uncertain (Zeithaml, 1981). This
process triggers anxiety and stress toward the product and the service provided by the
online retailer. Therefore, the customers will anticipate less satisfaction with the product
purchase if they perceive ambiguity in the purchasing process:
H4a. There is a negative relationship between perceived ambiguity and satisfaction with
the product purchase.
Similarly, individual’s emotions play a role when customers perceive risk in the purchasing
process which could impact their satisfaction ratings (Ramanathan, 2010). For example,
Chaudhuri (1997) indicates a strong relationship between risk perceptions and
negative consumption emotions. These same consumption-related emotions can also have
a direct effect on satisfaction and dissatisfaction (Mano and Oliver, 1993). Consequently,
customers’perception of risk may have a negative impact on anticipated satisfaction with
the product purchase:
H4b. There is a negative relationship between perceived riskiness and satisfaction with
the product purchase.
The effect of uncertainty on purchase intentions
Purchase intentions is a customer’s willingness to purchase a good or service from the online
retailer. Confidence is one of the determinants of purchase intentions according to Howard and
Sheth’s (1969) study. Recalling the modern psychology research (Fox et al., 2015) on natural
language, they find that the term confidence is used as a representation of perceived
ambiguity. Bennett and Harrell (1975) suggest that confidence plays an important role in
predicting intentions to purchase. Results indicate that if the customers are not confident
about the product, they are less likely to purchase. Hazen et al. (2012) find that if customers
are intolerant of ambiguity, they are less willing to pay for the remanufactured product.
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These results suggest that higher perceived ambiguity may be related to lower purchase
intentions. Therefore, the following hypothesis is generated:
H5a. There is a negative relationship between perceived ambiguity and purchase
intentions.
Vijayasarathy and Jones (2000) find that customers’perception of risk is an important factor
that influences intentions to shop online. For example, customers perceive a low level of risk
in Amazon.com because it is a well-known internet vendor and is among the pioneers of
e-commerce (Gefen, 2000). Lim (2003) suggests that customers dislike dealing with unknown
vendors because they are afraid that their credit cards will be misused, while many
customers perceive less risk in a reputable business and rely on references from other people
instead of individual trial and error. On the contrary, if the customers perceive higher level
of risk which indicates a higher probability of an undesirable outcome, they are less likely to
purchase the product from the online retailer. Hence, this study hypothesizes the following:
H5b. There is a negative relationship between perceived riskiness and purchase
intentions.
Based on these hypotheses, the conceptual model that we will test is presented in Figure 1.
Methodology
Experimental design and sample
To test the theoretically derived priori hypotheses concerning delivery time, shipping
charges, and purchase importance, this research presented a scenario-based experiment
(Rungtusanatham et al., 2011). It utilized a 3 (delivery time: two to six days vs four days vs
two days) ×2 (shipping charges: free shipping vs paid shipping) ×2 (purchase importance:
low vs high) between-subject factorial design which yielded 12 experimental treatments that
summarized in Table I. All participants were randomly assigned to one of the 12 scenarios.
Shipping
charges
Outcomes
• Satisfaction
• Purchase intention
Uncertainty
• Perceived ambiguity
• Perceived riskiness
Purchase
importance
Delivery time Figure 1.
Conceptual model
Table I.
Experimental
treatments
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A total of 360 adults from the USA participated in the web-based study recruited via the
Amazon Mechanical Turk national customer online panel (MTurk) and each treatment had
30 subjects. Three selection criteria have been used to select subjects: subjects are from the
USA, the Human Intelligence Task (HIT) approval rate for all requester’s is greater than or
equal to 95 percent, and the number of HITs approved for each subject exceeds 1,000.
The age of the sample ranged from 20 to 87, with a mean age of 41.84 years, and 65.4 percent
of the participants were female.
The scenario was about participants planning to buy a birthday gift from an online
retailer for their mom’s birthday. First, participants read the scenario that mom’s
75th birthday was in four days and they were planning to buy a tea pot which was one of
mom’s favorite as birthday gift. Depending upon the experimental treatment, participants
saw the shipping label stating whether this product was shipped free or they needed to pay
$3.99 shipping fees. In order to avoid the total price difference as a confounding factor,
the total price of tea pot in the free shipping treatment was equal to the price of the tea pot
plus the shipping fee in the paid shipping treatment. Purchase importance was also
manipulated by either stating that she would be disappointed if she did not get the gift on
her birthday (high importance) or by stating that an alternative gift could be purchased
from a local store (low importance). After exposure to the scenario, participants completed a
brief questionnaire which included the measure of dependent variables, control variables,
and manipulation checks. Examples of the scenarios are shown in Appendix 1.
Dependent variable and manipulation check measures
The four continuous dependent variable measures were adapted from the literature and
Appendix 2 provides an overview of themeasures used with factor loadings and Cronbach’sα.
Perceived ambiguity was measured with a six-item scale that was adapted from Venkatraman
et al. (2006). All items were measured on a seven-point Likert-scale (1 ¼absolutely uncertain/
7¼absolutely certain or 1 ¼strongly disagree/7 ¼strongly agree). A high score indicated a
high level of perceived ambiguity. Perceived riskiness was measured with a four-item scale
(1 ¼very safe/7 ¼very risky or 1 ¼strongly disagree/7 ¼strongly agree) that was adapted
from Venkatraman et al. (2006). A high score indicated a high level of perceived riskiness.
The satisfaction scale was adapted from Rafferty and Griffin (2009). The scale included three
items measu red on a seven-point Like scale (1 ¼strongly disagree/7 ¼strongly agree).
Purchase intentions was measured by one item on a seven-point Like scale (1 ¼strongly
disagree/7 ¼strongly agree) that was adapted from Harlam et al. (1995). A high score indicated
ahighpurchaseintentions.
To assess whether the experimental manipulations held, we included a one-item measure
for delivery time (“How long is my shipping?”two to six days, four days, or two days) and
shipping charges (“I get free shipping for my product”Yes or No). A one-item measure (this
is an important product that I have to have it on time) was used for perceived purchase
importance with a seven-point Likert scale (1 ¼strongly disagree/7 ¼strongly agree).
Three control variables were included in the experiment: risk attitude, intolerance of
ambiguity, and the cognitive reflection test (CRT).
Convergent and discriminant validity assessments
Convergent and discriminant validity of dependent variables were assessed through
confirmatory factor analysis (CFA) in LISREL (Table II). The CFA fit statistics supported
our model (Kline, 2015) with χ
2
¼224.94, df ¼43, comparative fit index ¼0.96, root mean
square error of approximation ¼0.08, and goodness-of-fit index ¼0.91.
Convergent validity was established through the average variance extracted (AVE) and
Cronbach’sα. The AVE for each factor exceeded the recommended threshold
of 0.5 (Nunnally and Bernstein, 1994) and all Cronbach’sαvalues exceeded 0.75.
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Discriminant validity of the factors was established through comparing the ϕ
2
correlation of
each factor pair with their respective AVE. Each pair of factors AVE exceeded ϕ
2
that
indicated discriminant validity.
Manipulation checks
To assess whether participants were aware of their respective experimental conditions
(Bachrach and Bendoly, 2011), a two-way contingency table analysis was conducted to
evaluate delivery time and shipping charges. The significance and large effect sizes of
the manipulation checks for delivery time and shipping charges confirmed the validity of the
manipulations (Miller and Acton, 2009). Seven participants failed the manipulation check and
were removed from the sample which resulted in a sample size of 353 subjects. To check
whether the manipulation of purchase importance was valid or not, an independent sample
t-test was conducted. Participants in the high purchase importance treatment perceived a
statistically significant greater importance of the product (M¼6.19, SD ¼1.26) than those in
the low purchase importance treatment (M¼2.93, SD ¼1.47, t(351) ¼22.45, po0.001).
Realism checks
Realism of the experimental treatment conditions in this study was evaluated by using two
items developed from Dabholkar (1994). Participants were asked whether the scenario was
realistic and whether they could imagine themselves in the situation. The realism check
indicated that participants considered the scenarios to be engaging and realistic with an
average score of 5.86 on a seven-point Likert scale. The one sample t-test was significant
(po0.001). This finding suggested that participant perceptions of the scenario
manipulations were realistic enough to evoke authentic behavioral responses.
Results
To test the hypotheses, PROCESS was used to explore the moderated-mediation
relationship. PROCESS are newly developed SPSS macros that allow for the testing of
mediation, moderation, and conditional process analysis. It is based on ordinary least square
regression path analysis and is suitable to estimate various types of models, from simple
mediation or moderation to more advanced moderated mediation models (Hayes, 2013).
We ran PROCESS model 10 (Hayes, 2013) which matches the layout of our conceptual model
(with two moderators between the independent variable and the proposed mediator
variables and dependent variables).
Scale Item Standard loading Cronbach’sαAVE
Perceived ambiguity PA1 0.606 0.867 0.684
PA2 0.879
PA3 0.829
PA4 0.874
PA5 0.836
PA6 0.706
Perceived riskiness PR1 0.758 0.787 0.510
PR2 0.876
PR3 0.730
PR4 0.765
Satisfaction SAT1 0.924 0.873 0.713
SAT2 0.849
SAT3 0.915
Notes: χ
2
¼224.94; df ¼43; CFI ¼0.96; RMSEA ¼0.08; GFI ¼0.91
Table II.
CFA results
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Results for perceived ambiguity, satisfaction, and purchase intentions
The first model had delivery time as an independent variable, shipping charges and
purchase importance as two moderators, perceived ambiguity as proposed mediator, and
satisfaction or purchase intentions as dependent variables, controlling for gender, age, risk
attitude, intolerance of ambiguity, and cognitive mode (CRT). Results first suggested that
delivery time did not have a significant direct impact on satisfaction. H1a was not
supported. However, delivery time had a significant positive impact on perceived ambiguity
(β¼0.56, po0.001) such that individuals perceived higher level of ambiguity when
delivery time was longer than when delivery time was shorter. Therefore, H1c
was supported.
Shipping charges played a significant moderating role on the relationship between
delivery time and perceived ambiguity ( β¼0.36, p¼0.03). Figure 2 exhibits the interaction
effect which indicated that the influence of delivery time on perceived ambiguity is
strengthened by paid shipping. When subjects paid a shipping fee, they perceived lower
ambiguity when the delivery time was short, but perceived higher ambiguity when the
delivery time was lengthy. Therefore, H2a was partially supported. However, the interaction
effect of delivery time and shipping charges was not significant on satisfaction. Therefore,
H2c was not supported.
Purchase importance played a significant moderating role between delivery time
and satisfaction with β¼0.29, p¼0.02 controlling for the indirect effect of perceived
ambiguity. Figure 3 exhibits the interaction effect that when the purchase was important,
subjects were significantly less satisfied when delivery time was lengthy. Therefore, H3c
was supported. However, the interaction effect of delivery time and purchase importance
was not significant on perceived ambiguity. Therefore, H3a was not supported.
Delivery time had a significant negative impact on purchase intentions with β¼0.22,
p¼0.05 that subjects were less willing to purchase the product if the delivery time was
lengthy. Therefore, H1b was supported. The interaction effects of delivery time and
shipping charges and the interaction of delivery time and purchase importance were not
significant on purchase intentions. Therefore, H2d and H3d were not supported.
In addition, results of the effect of perceived ambiguity on satisfaction and purchase
intentions indicated that perceived ambiguity had a significant impact on satisfaction with
the product ( β¼−0.58, po0.001) such that participants felt less satisfied if they perceived
a higher level of ambiguity. Therefore, H4a was supported. Also, perceived ambiguity had a
significant impact on purchase intentions ( β¼−0.67, po0.001), suggesting that subjects
were less likely to purchase the product if they perceived a higher level of ambiguity.
Therefore, H5a was supported.
3.6
3.4
3.2
2.6
2.8
2.4
2.2
3
2Short shipping
time
Medium shipping
time
Lengthy shipping
time
Free shipping Paid shipping
Figure 2.
Interaction of delivery
time and shipping
charges on perceived
ambiguity
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Results for perceived riskiness, satisfaction, and purchase intentions
The second model had delivery time as an independent variable, shipping charges and
purchase importance as two moderators, perceived riskiness as proposed mediator, and
satisfaction or purchase intentions as dependent variables, controlling for risk attitude,
intolerance of ambiguity, and CRT. Results provided evidence that delivery time had a
significant positive impact on perceived riskiness ( β¼0.37, p¼0.02) such that individuals
perceived higher riskiness when the delivery time is lengthy compared to short delivery
time. Therefore, H1d was supported. However, neither the interaction of delivery time and
shipping charges nor the interaction of delivery time and purchase importance was
significant on perceived riskiness. Thus, H2b and H3b were not supported.
Further, results of the effect of perceived riskiness on satisfaction and purchase
intentions indicated that perceived riskiness had a significant impact on satisfaction with
the product ( β¼−0.22, po0.001) such that participants felt less satisfied if they perceived
a higher level of risk. Therefore, H4b was supported. Results also indicated that perceived
riskiness had a significant impact on purchase intentions ( β¼−0.25, po0.001), suggesting
that subjects were less likely to purchase the product if they perceived a higher level of risk.
Therefore, H5b is supported.
Mediation effect
Next is to test whether perceived ambiguity and perceived riskiness serve as possible
mediators of delivery time to satisfaction/purchase intentions relationship and whether
shipping charges and purchase importance are moderators of the relationship between
delivery time and perceived ambiguity/riskiness which is the proposed mediator. Results
suggest that there were statistically significant conditional effects across all moderator
levels which simplified the model to a simple mediation model. Model 4 in PROCESS with
perceived ambiguity as a mediator and satisfaction as the dependent variable was used.
Results show that there is a statistically significant indirect effect, β¼−0.26. To assess the
significance of the observed indirect effect, we used bootstrap confidence intervals
(Hayes, 2013). We ran a bootstrap confidence interval with 5,000 sample estimates.
Bootstrapping yielded a 95 percent confidence interval for the indirect effect of 0.10 to 0.35,
and because the confidence interval did not contain 0, it was concluded that the indirect
effect was significant and negative. This suggested that research participants exposed to
5.9
6
5.8
5.7
5.4
5.5
5.3
5.2
5.6
Short shipping
time
Medium shipping
time
Lengthy shipping
time
High purchase importance Low purchase importance
Figure 3.
Interaction of delivery
time and purchase
importance on
satisfaction
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the slower delivery time were less likely to be satisfied with the product than those who had
a faster delivery time and this effect was transmitted through perceived ambiguity.
Similarly, perceived riskiness played a significant mediating role between delivery time and
satisfaction, β¼−0.10 with the confidence interval [−0.16, −0.05]. Results indicated that
participants exposed to the slower delivery time were less likely to be satisfied with the
product than those who had a faster delivery time and this effect was transmitted through
perceived riskiness.
Second, for the purchase intentions, perceived ambiguity was a significant mediator,
β¼−0.30 with confidence interval [−0.40, −0.22]. This suggested that research participants
exposed to the slower delivery time were less likely to purchase the product than those who
had a faster delivery time and this effect was transmitted through perceived ambiguity.
Also, perceived riskiness was a significant mediator, β¼−0.10 with confidence interval
[−0.17, −0.05]. This suggested that research participants exposed to the slower delivery
time are less likely to purchase the product than those who have a faster delivery time and
this effect was transmitted through perceived riskiness.
Post hoc analysis
Further, two sets of pairwise comparisons were conducted to explore the effect of delivery
time on perceived ambiguity and perceived riskiness under each level of shipping charges
and purchase importance.
First is the comparison of the effect of delivery time on perceived ambiguity across the
two shipping charge treatments. Results show that when subjects had to pay for shipping,
there was a significant difference in perceived ambiguity among delivery times: participants
who were exposed to the fastest delivery treatment perceived a lower level of ambiguity
(M¼2.12, SD ¼0.14) than those whose delivery time was medium (M¼2.70, SD ¼0.14,
p¼0.009). Participants who were exposed to the medium delivery time perceived a lower
level of ambiguity (M¼2.70, SD ¼0.14) than those who had the slowest delivery (M¼3.36,
SD ¼0.14, po0.001). In the free shipping treatment participants who received a fast
delivery (M¼2.48, SD ¼0.14) perceived significantly lower ambiguity than those who
received slow delivery (M¼3.05, SD ¼0.14, p¼0.01) (Figure 4), but there was no significant
difference between fast and medium and medium and slow delivery.
Moreover, the effect of delivery time on perceived ambiguity varied across purchase
importance treatments. When the purchase was less important, participants who had fast
delivery (M¼2.37, SD ¼0.14) perceived a lower level of ambiguity than those who had a
medium delivery (M¼2.74, SD ¼0.14, p¼0.05); but there was no significant difference of
perception of ambiguity between slow and medium delivery. Similarly, when the purchase
was important, participants who had a fast delivery (M¼2.22, SD ¼0.14) perceived a lower
level of ambiguity than those who had a medium delivery (M¼2.63, SD ¼0.14, p¼0.01);
0
0.5
1
1.5
2
2.5
3
3.5
4
Free shipping
Paid shipping
Perceived ambiguity
Shipping charges
2 days
4 days
2-6 days
Figure 4.
Post hoc analysis of
the effect of delivery
time on perceived
ambiguity across
shipping charges
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but there was no significant difference of perception of ambiguity between slow and
medium speed of delivery (Figure 5).
Next is a comparison of the effect of delivery time on perceived riskiness varied across
shipping charges treatments. When participants paid shipping cost, those who were
exposed to the fastest delivery treatment perceived lower level of riskiness (M¼3.61,
SD ¼0.18) than those whose delivery speed was slow (M¼4.45, SD ¼0.18, p¼0.002).
Similarly, for the free shipping treatment participants who received a fast delivery
(M¼3.43, SD ¼0.18) perceived significantly lower riskiness than those who received a
medium delivery (M¼4.20, SD ¼0.18, p¼0.007) (Figure 6).
Finally, a comparison of the effect of delivery time on perceived riskiness varied across
purchase importance treatments. When the purchase was less important, results indicate
that participants who had fast shipping (M¼3.28, SD ¼0.18) perceived a lower level of risk
than those who had slow delivery (M¼4.07, SD ¼0.18, p¼0.004); participants who had fast
shipping (M¼3.28, SD ¼0.18) perceived a lower level of risk than those who had medium
delivery (M¼4.14, SD ¼0.18, p¼0.002); and there was no significant difference of risk
perceptions between slow and medium speeds of delivery. In the high purchase importance
treatment, participants who had fast delivery (M¼3.76, SD ¼0.18) perceived lower level of
riskiness than those who had slow delivery (M¼4.62, SD ¼0.18, p¼0.002). However, there
was no significant difference between either low or medium delivery time or between
medium and high delivery time (Figure 7).
General discussions and managerial insights
While expressing concern about “last mile”delivery (Esper et al., 2003), existing literature on
e-commerce and logistics has largely neglected the relative importance of the impact of each
dimension of logistics service quality. Further, with increased online shopping and home
delivery, shipping charges and purchase importance take a crucial role in customer
0
0.5
1
1.5
2
2.5
3
3.5
HighLow
Perceived ambiguity
Purchase importance
2 days
4 days
2-6 days
Figure 5.
Post hoc analysis of
the effect of delivery
time on perceived
ambiguity across
purchase importance
0
1
2
3
4
5
Paid shipping Free shipping
Perceived riskiness
Shipping charges
2 days
4 days
2-6 days
Figure 6.
Post hoc analysis of
the effect of delivery
time on perceived
riskiness across
shipping charges
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shopping behaviors and experience. Therefore, this study focused on the impact of delivery
time –the most relevant dimension of logistics service quality in an e-commerce shopping
environment –on customer satisfaction and purchase intentions. Moreover, we also
examined the interaction effect of delivery time and shipping charges, as well as delivery
time and purchase importance. In addition, in order to understand how delivery time can
alter customer satisfaction and behavior, we proposed perceived ambiguity and perceived
riskiness acting as the mechanism that can explain the “how”.
Our experiment offers several interesting findings. First, we did not observe that delivery
time, which is one of the dimension of logistics service quality, had a significant impact on
customer satisfaction. We did, however, find a significant impact on purchase intentions.
We did see that significant effect on both satisfaction and purchase intentions were
transmitted through perceived ambiguity and perceived riskiness. Delivery time
significantly increased customer’s uncertainty perceptions in the form of perceived
ambiguity and perceived riskiness which significantly decreased satisfaction and purchase
intentions. This result indicates that delivery time determines whether or not the customer
will make the purchase. If online retailers also intend to improve customer satisfaction at the
time of the purchase, they have to look beyond quicker shipping. Therefore, they may want
to ensure reasonable delivery time with availability of the item, or order tracking (Rao et al.,
2011) to satisfy customers. In addition, perceived ambiguity and perceived riskiness are
important to customers. How to reduce uncertainty is also important to online retailers.
Even though shippers such as UPS can provide tracking information, this only happens
after the purchase. When customers make the purchase, they lack information to predict
whether or not delivery will be on time. Therefore, offering a guaranteed delivery time
rather than two six business days may reduce perceived ambiguity, and offering a money-
back guarantee may reduce perceived riskiness.
There are further implications for Omni-channel retailers. Customers are “time starved.”
If they can order online to save search cost and pick up in store, especially with same day
pick up, it becomes the customer’s decision to choose when to get the product. They may not
feel like they lack information to predict when they will receive the product and less likely to
suffer from a loss by purchasing the product. Hence, they may perceive less uncertainty
during the purchase process that increases satisfaction and purchase intentions.
Second, shipping charges matter! Keeping in mind that our experiment compared
products with the same total cost, the “free shipping”framing strengthens the customer’s
perceived ambiguity with a short delivery time. This indicates that customers are willing to
pay for shipping when retailers can offer shorter deliver time and less willing to pay for
shipping when delivery time is lengthy. The implication is that online retailers can use free
shipping as an alternative to longer shipping time. However, they need to be very careful
about whether or not to display a separate charge for shipping when delivery time is
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
HighLow
Perceived riskiness
Purchase importance
2 days
4 days
2-6 days
Figure 7.
Post hoc analysis of
the effect of delivery
time on perceived
riskiness across
purchase importance
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lengthy. Our result suggests that it is not because customer does not mind paying for
shipping with a short delivery time, but that they perceive higher ambiguity when delivery
time is lengthy and they pay a separate shipping cost. If online retailers can offer shorter
delivery time, they may choose to charge for shipping by showing lower product price to
attract customers. For example, most websites offer a sort feature: “sort by –recommended,
price low to high, price high to low, etc.”By offering a lower product price, customers may
see the product on the first page of the website and increase the possibility of purchase.
Further, it is advisable for the retailer to charge shipping when they offer a shorter delivery
time which can reduce customer’s perceived ambiguity and may increase satisfaction.
Therefore, free shipping is not always good. However, if they cannot offer fast shipping,
online retailers may offer free shipping instead which means including the shipping charges
in the product price to reduce perceived ambiguity that can increase satisfaction and
purchase intentions. In this way, online retailers earn the same amount of profit for each unit
along with higher customer satisfaction and purchase intentions.
Third, purchase importance matters for the online shopping in terms of satisfaction,
but does not matter for purchase intentions. When the purchase is important, customers
are more satisfied when they get faster delivery. However, this does not impact their
purchase intentions. It may be that customers have already assessed the importance of the
purchase and do not want to risk the purchase by purchasing it online. They are more
likely to purchase important products in-store with the visual and physical touches.
This opens a door for Omni-channel retailers to offer order it online with same day pick up
in store. Customers can still purchase the product online, but can get an important product
the same day or within a short period of time. This ensures that customers receive
important products on time. Offering these options may increase sales for retailers and
convenience for customers.
Fourth, when shipping is free, there is no significant difference between medium delivery
time ( four days) and lengthy delivery time (two to six days) on both perceived ambiguity
and perceived riskiness. This result suggests that either fast or slow delivery time is
important to the online retailers. Medium delivery time does not help. Therefore, if the online
retailer cannot offer fast delivery, they may want to consider slow delivery with lower cost
rather than medium delivery time (or as fast as possible). This can reduce total shipping cost
for the online retailers.
Theoretical implications
With these interesting findings, this paper makes a twofold contribution toward
advancing the extant literature. First, while there have been studies in the past that have
shown that logistics service quality in electronic B2C transactions is a critical determinant
of customer satisfaction and purchase intentions, this paper sheds light on how delivery
time –the most relevant dimension of the logistics service quality in e-commerce
environment –interacts with shipping charges and purchase importance to impact
customers’satisfaction and purchase intentions. Previous research has shown that
logistics services impact customer satisfaction (Rao et al., 2011; Griffis et al., 2012).
Our results are consistent with this literature that has shown that delivery time by itself
cannot alter customer satisfaction, but has a significant influence on purchase intentions.
This paper provides two less intuitive findings with interesting managerial implications,
namely the interaction effect of delivery time and shipping charges which are both
characteristics of service offerings, and the interaction effect of delivery time which is a
logistics service characteristic and purchase importance which is a customer purchase
characteristic (or individual difference for a particular shopping episode). Second,
uncertainty in the form of perceived ambiguity and perceived riskiness is introduced to
the logistics service literature as the mechanism that explains how delivery time
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interactingwithshippingchargesandpurchaseimportanceimpactcustomersatisfaction
and purchase intentions. With the understanding of the mediating role of perceived
ambiguity and perceived riskiness, it offers the potential directions that online retailers
can adopt to ameliorate negative effects on customer satisfaction and purchase intentions.
Any ameliorative measures would need to reduce uncertainty as this is the
means whereby the characteristics of retailer logistics service offerings transmit to
customer outcomes.
Limitations and further research
This study, while providing some key insights into the mechanisms that can explain
customer satisfaction and purchase intentions in online retail, has limitations.
First, purchase intentions in this study was measured by a self-reported item, which may
deviate from customers’actual purchase behavior. Therefore, future research may need to
conduct research in the field to predict whether customers are less willing to purchase the
real product if they perceived higher ambiguity or higher risk. Second, Thirumalai and
Sinha (2005) indicate that product types matter. This study did not take the product type
into consideration. Further research may explore product characteristics associated with
each shipping options that can influence customer’s perception of ambiguity and risk, and
thus increase their satisfaction and purchase intentions. Third, it becomes more and more
popular to offer threshold shipping charges rather than free shipping or paid shipping for
the whole order. Therefore, further research may explore how online retailers set up their
shipping strategy to reduce the total cost and better serve customers.
In conclusion, this research sheds light on how a dimension of logistics service
quality –delivery time, along with shipping charges and purchase importance –interact to
impact customer satisfaction and purchase intentions. It does so by showing that the
psychological mechanism of uncertainty in the form of perceived ambiguity and perceived
riskiness underlies the customer decision process. Thus, the research both builds theory and
has insight for practitioners.
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Appendix 1. Samples of the scenario
Appendix 2. Measurement of dependent, control, and manipulation variables
Perceived ambiguity (adapted from Venkatraman et al., 2006)
How certain are you that you will get the gift on time?
a
I have all the relevant information I need to make the decision to purchase the gift.
I am confident that I know the expected outcomes (arrive on time or not) for the decision to
purchase the gift.
I have sufficient information to make the decision to purchase the gift.
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I fully understand the expected outcomes from the decision to purchase the gift.
I need more information to make the decision to purchase the gift.
Perceived riskiness (adapted from Venkatraman et al., 2006)
It is very likely that I will lose money in making the decision to purchase gift if it does not arrive on time.
In making the decision to purchase the gift, I would worry about the consequence.
I could incur a great loss (disappointing my Mom) by making the decision to purchase the gift if it
does not arrive on time.
How risky are the decisions you made to purchase the gift?
b
Satisfaction (adapted from Rafferty and Griffin, 2009)
All in all, I am satisfied with this gift I purchased.
I do not like this gift I purchased.
I am happy to purchase this gift.
Purchase intention (adapted from Harlam et al., 1995)
I am willing to purchase this gift.
Risk attitude (Burton et al., 1998)
I do not like to take risks.
Compared to most people I know, I like to “live life on the edge.”
I have no desire to take unnecessary chances on things.
Compared to most people I know, I like to gamble on things.
Intolerance of ambiguity (adapted from Buhr and Dugas, 2002)
Ambiguity stops me from having a firm opinion.
Ambiguity makes life intolerable.
It frustrated me to not have all the information I need.
When I am in an ambiguous situation, I cannot go forward.
When I am in an ambiguous situation, I cannot function well.
I must get away from all ambiguous situation.
I cannot stand being undecided about my future.
Cognitive reflection test (Moritz et al., 2013)
A bat and a ball cost $1.10 in total. The bat costs $1 more than the ball. How much does the ball cost?
____cents
If it takes 5 machines 5 min to make 5 widgets, how long would it take 100 machines to make
100 widgets? ____minutes
In a lake, there is a patch of lily pads. Every day, the patch doubles in size. If it takes 48 days for the
patch to cover the entire lake, how long would it take for the patch to cover half the lake? ____days.
Note: Cronbach’sαindicates internal consistency reliability. All other items anchored: strongly
disagree, mostly disagree, somewhat disagree, neither agree nor disagree, somewhat agree, mostly
agree, and strongly agree except for cognitive reflection test. Principal components extraction and
Varimax rotation were performed.
a
Anchor points labeled: absolutely uncertainty, mostly uncertain, somewhat uncertain, neither
certain nor uncertain, somewhat certain, mostly certain, and absolutely certain.
b
Anchor points labeled: very safe, mostly safe, somewhat safe, neither risky nor safe, somewhat
risky, mostly risk, and very risky.
Corresponding author
Siqi Ma can be contacted at: sma@walton.uark.edu
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