Understanding e-commerce customer behaviors to use drone delivery services: A privacy calculus view

Figures & data

Figure 1. Research model.

Table 1. Demographics

Demographics	Statistics
Gender	Female = 28 (34%) Male = 55 (66%)
Transportation	YES = 77 (93%๗ NO = 5 (7%)
Drone proximity	YES = 4 (6%) NO = 78 (94%)
Amazon prime membership	NO = 6 (7%) Had = 14 (17%) YES = 63 (76%)
Neighborhood types	Tight = 27 (32%) Close = 30 (36%) Moderately close = 23 (28%) Distanced = 3 (4%)
Delivery frequency	Average = 1.8/per month
Shopping distance proximity	Average = 7.13 minutes/per month

Table 2. Constructs and items

Construct	Code	Items	Reference
Relative advantage in speed	AdvS1	Drone delivery is a speedy way to deliver orders	Al-Jabri and Sohail (2012)
	AdvS2	Drone delivery allows me to receive orders quickly	Moore and Benbasat (1991)
	AdvS3	Drone delivery is useful in shortening the order delivery time	Moore and Benbasat (1991)
	AdvS4	Drove delivery gives greater control over the speed of my delivery	Moore and Benbasat (1991)
Relative advantage in environment protection	AdvEn1	Drone delivery is a green way to deliver orders	Al-Jabri and Sohail (2012)
	AdvEn2	Drone delivery allows me to receive orders in a more environmentally friendly way	Moore and Benbasat (1991)
	AdvEn3	Drone delivery is a less carbon dioxide emitting delivery method	Moore and Benbasat (1991)
	AdvEn4	Drove delivery gives me greater control to protect the climate	Moore and Benbasat (1991)
Privacy vulnerability	PrivVul1	My private information is vulnerable to security breaches if I use drone delivery	Ifinedo (2012)
	PrivVul2	My private sphere is subject to security vulnerability if I use drone delivery	Vance et al. (2012)
	PrivVul3	Privacy violation to my data could occur at drone delivery.	Vance et al. (2012)
	PrivVul4	Trespassing my privacy sphere is likely if I use drone delivery.	Vance et al. (2012)
Privacy risk severity	PrivRskS1	Protecting my private information in drone delivery is important	Vance et al. (2012)
	PrivRskS2	Threats to the security of my private information in drone delivery is harmful	Vance et al. (2012)
	PrivRskS3	In terms of security risks in drone delivery, the vulnerability of my private information is low	Vance et al. (2012)
	PrivRskS4	Loss of private data from hacking in drone delivery is a serious problem for me	Vance et al. (2012)
Technology anxiety	Tanxi1	I will avoid using drone delivery because it is unfamiliar to me	Lee and Yang (2013)
	Tanxi2	I have difficulty understanding most technological matters of drone delivery	Meuter et al. (2003)
	Tanxi3	I feel apprehensive about using technology	Meuter et al. (2003)
	Tanxi4	Drone delivery terminology sounds like confusing jargon to me	Meuter et al. (2003)
	Tanxi5	I hesitate to use drone delivery for fear of making mistakes I cannot correct	Meuter et al. (2003)
Switch intention to drone delivery	SI1	Rate the likelihood that you would switch from truck delivery to drone delivery: Unlikely—Likely	Bansal et al. (2005)
	SI2	Rate the probability that you would switch from truck delivery to drone delivery: Improbable—Probable	Meuter et al. (2003)
	SI3	Rate the chance that you would switch from truck delivery to drone delivery: No chance—Certain	Meuter et al. (2003)

Table 3. Items’ loadings

Items	EP	SPD	PRS	PRV	SI	ANX
EP1	0.849
EP2	0.916
EP3	0.811
EP4	0.827
SPD1		0.861
SPD2		0.905
SPD3		0.884
SPD4		0.81
PRS1			0.87
PRS2			0.903
PRV1				0.82
PRV2				0.838
PRV3				0.846
PRV4				0.858
SI1					0.963
SI2					0.957
SI3					0.938
ANX1						0.736
ANX2						0.832
ANX3						0.807
ANX4						0.812

Table 4. Construct reliability and validity

	Cronbach’s Alpha	rho_A	Composite Reliability	Average Variance Extracted (AVE)
EP	0.874	0.886	0.913	0.726
SPD	0.888	0.896	0.923	0.749
PRS	0.728	0.738	0.88	0.786
PRV	0.874	0.963	0.906	0.707
SI	0.949	0.949	0.967	0.908
ANX	0.809	0.811	0.875	0.636

Table 5. Fornell-Larcker criterion discriminant validity

	EP	SPD	PRS	PRV	SI	ANX
EP	0.852
SPD	0.314	0.865
PRS	0.268	0.235	0.886
PRV	0.257	0.19	0.623	0.841
SI	0.388	0.489	0.069	0.055	0.953
ANX	−0.192	0.125	0.297	0.307	−0.16	0.798

Table 6. Model statistics & hypotheses results

Model path statistics	Path coefficients	Hypothesis supported?	P values
EP -> SI	0.216	Yes	0.039
SPD -> SI	0.443	Yes	0.000
ANX -> SI	−0.174	No	0.103
PRV -> ANX	0.199	No	0.153
PRS -> ANX	0.173	No	0.321

Figure 2. A pictorial representation of the HTMT result of bootstrapping.

Table 7. Bootstrapping test result

HTMT Intervals	Original Sample (O)	Sample Mean (M)	LL 2.50%	UL 97.50%
AdvSpeed -> AdvEnv	0.35	0.356	0.121	0.612
Intention -> AdvEnv	0.419	0.415	0.174	0.622
Intention -> AdvSpeed	0.527	0.522	0.316	0.686
PrivacyRskS -> AdvEnv	0.343	0.357	0.132	0.617
PrivacyRskS -> AdvSpeed	0.282	0.315	0.116	0.529
PrivacyRskS -> Intention	0.094	0.172	0.021	0.147
PrivacyVul -> AdvEnv	0.297	0.318	0.117	0.537
PrivacyVul -> AdvSpeed	0.224	0.259	0.102	0.42
PrivacyVul -> Intention	0.097	0.166	0.034	0.124
PrivacyVul -> PrivacyRskS	0.769	0.77	0.552	0.93
TAnxiety -> AdvEnv	0.221	0.269	0.095	0.43
TAnxiety -> AdvSpeed	0.151	0.221	0.063	0.193
TAnxiety -> Intention	0.181	0.235	0.072	0.313
TAnxiety -> PrivacyRskS	0.378	0.403	0.158	0.607
TAnxiety -> PrivacyVul	0.31	0.33	0.159	0.504

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