Market orientation and SME performance: Moderating role of IoT and mediating role of creativity

Figures & data

Figure 1. Conceptual framework.

Table 1. Profile of the respondents and firm.

Categories		Frequency	Percentage
Gender	Male	174	76%
	Female	49	21.40%
	Prefer not to say	6	2.60%
Age	18–24	21	9.20%
	25–34	65	28.40%
	35–44	79	34.50%
	45–54	49	21.40%
	54+	15	6.60%
Education	Primary school	1	0.40%
	High school	32	14%
	College	79	34.50%
	Undergraduate degree	75	32.80%
	Master’s degree	39	17%
	Professional qualification	3	1.30%
Position	Owner/CEO/proprietor	110	48%
	Director	52	22.70%
	Manager	62	27.10%
	Supervisor	5	2.25
Turnover	Under €2 million	170	74.20%
	€2 million to €10 million	38	16.60%
	€10+ million to €50 million	16	7%
	Over €50 million	5	2.20%
Employees	Under 10	152	66.40%
	10 to 49	52	22.70%
	50 to 249	25	10.90%
Industry	Retail	155	67.70%
	Manufacturing	15	6.60%
	Service	59	25.80%
Firm Age	Under 3 years	52	22.70%
	3 to 5 years	55	24%
	6 to 10 years	49	21.40%
	11 to 15 years	26	11.40%
	Over 15 years	47	20.50%
Total		229	100%

Table 2. Measurement scale and reliability statistics.

Construct, Items and Code, Source and Reliability Statistics	Loading (EFA^a)	Loading (CFA^b)
Market Orientation (MO): (Baker & Sinkula, 2005) (Cronbach’s Alpha^c = .94; AVE^d .709; CR^e = .935)
We are slow to detect changes in our customers’ product preferences. (MO1)	.74	.81
We frequently review the likely effect of changes in our business environment on customers. (MO2)	.91	.92
When something important happens to a major customer or market, the whole business unit is informed about it within a short period. (MO3)	.64	.69
When one department finds out something important about competitors, it is slow to alert other departments. (MO4)	.94	0.93
For one reason or another, we tend to react slowly to changes in our customers’ product or service needs. (MO5)	.93	.88
Several departments get together periodically to plan a response to changes taking place in our business environment. (MO6)	.72	.79
Internet of Things (IoT): (Kim et al., 2012) (Cronbach’s Alpha = .92; AVE = .636; CR = .924)
There is a stable network connection between IoT devices. (IoT1)	.71	.75
Interconnectivity of IoT helps to efficiently manage system resources. (IoT2)	.83	.83
Interconnectivity between IoT devices helps to provide more effective coordination among different functional activities. (IoT3)	.92	.88
It helps in effective assimilation of new information and knowledge to assist in decision-making process. (IoT4)	.83	.85
We constantly monitor the performance of IT functioning. (IoT5)	.87	.79
Our employees are very knowledgeable about the role of IoT. (IoT6)	.57	.71
Our employees show superior ability to learn about new technologies. (IoT7)	.71	.75
Creativity (CR): (Sohn & Jung, 2010) (Cronbach’s Alpha = .84; AVE = .574; CR = .842)
We derive original ideas. (CR1)	.7	.78
We derive unique ideas. (CR2)	.59	.77
We derive large number of ideas. (CR3)	.95	.85
There is a variation in our ideas. (CR4)	.62	.62
Firm Performance (FP): (Baker & Sinkula, 2005; Lonial & Carter, 2015; Morgan et al., 2009) (Cronbach’s Alpha = .82; AVE = 0.673; CR = .889)
Our return on investment increased as compared to the competitors. (FP1)	.48	.58
Our sales increased as compared to competitors. (FP2)	.94	.93
New product/service development in our firm is higher as compared to the competitors. (FP3)	.94	.91
Customer service quality is improved as compared to the competitors. (FP4)	.81	.81

^aEFA = Exploratory Factor Analysis, ^bCFA = Confirmatory Factor Analysis, ^cCronbach’s Alpha = Reliability Statistics, ^dAVE = Average Variance explained; ^eCR = Construct Reliability.

Table 3. Descriptive statistics and correlation matrix.

Constructs	Mean	SD	MO	IoT	CR	FP
MO	4.03	.96	1
IoT	3.78	.97	.636*	1
CR	3.81	.96	.268*	.206*	1
FP	3.97	.94	.539	.060*	.346*	1

**Correlation is significant at the 0.01 level

Table 4. Discriminant validity.

Constructs	Correlation	Square Correlation	AVE*	AVE >Square Correlation
MO × IoT	.636	.404	.673	Yes
MO × CR	.268	.072	.642	Yes
MO × FP	.539	.291	.691	Yes
IoT × CR	.206	.042	.605	Yes
IoT × FP	.06	.004	.655	Yes
CR x× FP	.346	.120	.624	Yes

*AVE is the average of correlated costructs

Table 5. Multi-collinearity test.

	Standardized Coefficients			Collinearity Statistics
Constructs	Beta	t	Sig.	Tolerance	VIF
MO	0.212**	3.755	0	0.512	1.91
IoT	0.427***	7.094	0	0.502	1.892
CR	0.202***	3.429	0.001	0.765	1.212
IoT x MO	0.165**	2.768	0.006	0.778	1.321
IoT x CR	-0.035	-1.038	0.3	0.802	1.199

***Correlation is significant at the .001 level. **Correlation is significant at the .01 level.

Table 6. Summary of hypothesis testing.

Hypothesis	Standardized coefficient		Remark	R^2
	Direct effect	Indirect effect
Independent effects:
H1: MO → Firm performance (FP)	0.212**		Supported
H2: CR → Firm performance (FP)	0.202***		Supported
H4: IoT → Firm performance (FP)	0.427***		Supported
Mediation effects:
H3: MO → CR → Firm performance (FP)		0.057***	Supported (Partial mediation)
Moderating effects:
H5: IoT x MO → Firm performance (FP)	0.165**		Supported
H6: IoT x CR → Firm performance (FP)	-0.035		Not Supported
CR				0.069
Firm performance (FP)				0.449

***Significant at < .001 level, **Significant at < .01 level, R² = squared multiple correlations.

Figure 2. Interaction of IoT and MO.

Figure 3. Interaction of IoT and creativity.

Figure 4. Structural model.

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