Revolutionizing the runway: how technological and marketing innovation fuse market sensing on marketing performance in fashion industry

Figures & data

Table 1. Classifications of dimension sensing (S).

Authors	Approach	Dimension sensing (S)
Khan et al. (2022)	capability	sensing
Dias and Lages (2021)	capability	sense, sense-making, response
Bailey (2014)	information	sensing
Yu and To (2013)	information	sense
Sugiyarti and Ardyan (2017)	capability	sensing,

Figure 1. Conceptual research model.

Table 2. Location or area of fashion industry.

No	Regency / City	Number of business units	Percentage (%)
1	Jakarta	30	21
2	Bandung	25	18
3	Semarang	20	14
4	Solo	20	14
5	Surabaya	17	12
6	Sidoarjo	30	21
Total		142	100

Sources: Sample cluster by design research 2019.

Table 3. Result test of instrument.

Variables	Indicators	Validity testing			Reliability testing
		Pearson correlation	Sig	Remarks	Cronbach’s Alpha If deleted	Cronbach’s Alpha	Remarks
Sensing (S)	S1	0.947	0.000	valid	0.794	0.847	reliable
	S2	0.959	0.000		0.802
	S3	0.920	0.000		0.804
	S4	0.959	0.000		0.802
Sense-making (SM)	SM1	0.900	0.000	valid	0.787	0.836	reliable
	SM2	0.915	0.000		0.786
	SM3	0.865	0.000		0.800
	SM4	0.825	0.000		0.807
Response (R)	R1	0.815	0.000	valid	0.671	0.754	reliable
	R2	0.861	0.000		0.725
	R3	0.632	0.000		0.705
	R4	0.771	0.000		0.790
	R5	0.772	0.000		0.709
Technological Innovation (TI)	TI1	0.940	0.000	valid	0.789	0.836	reliable
	TI2	0.807	0.000		0.804
	TI3	0.852	0.000		0.801
	TI4	0.940	0.000		0.789
Marketing innovation (MI)	MI1	0.899	0.000	valid	0.780	0.820	reliable
	MI2	0.832	0.000		0.792
	MI3	0.912	0.000		0.770
	MI4	0.860	0.000		0.787
	MI5	0.826	0.000		0.788
Marketing performance (MF)	MF1	0.870	0.000	valid	0.782	0.818	reliable
	MF2	0.883	0.000		0.777
	MF3	0.832	0.000		0.786
	MF4	0.794	0.000		0.789
	MF5	0.901	0.000		0.776

Figure 2. Outer model.

Table 4. Evaluation of the PLS-SEM model.

Author (s)	Indicator	Remarks
Outer model (measurement model)
Ali et al. (2018	Indicator reliability	Outer loading > 0.7 for theoretical tests, and 0.5 – 0.7 for exploratory research
Ali et al. (2018); Franke and Sarstedt (2019); Hair, Hult, et al. (2017).	Discriminant validity	Cross-loading when indicator variables on latent variables have a bigger value than the valuation thirsting. Each latent variable must be higher than the correlation between latent variables.
Henseler (2017) ; Van Riel et al. (2017); Henseler et al. (2015).	Internal consistency	Composite reliability is more than 0.70 for theoretical tests and greater than 0.60 for exploratory research. Cronbach’s alpha >0.7 for theory tests or > 0.6 for exploratory research.
Henseler et al. (2015); Hair, Hult, et al. (2017)	Convergent validity	Value of AVE > 0.5.
Inner model (Structural model)
Ali et al. (2018)	Determination coefficient (R square)	R square > 0.75, it is good
	The significance and magnitude of the coefficients of the structural model	Significant

Table 5. Outer loadings.

	Sense	Sensemaking	Response	Technological innovation	Marketing innovation	Marketing performance
S1	0.982
S2	0.956
S3	0.972
S4	0.962
SM1		0.967
SM2		0.972
SM3		0.949
SM4		0.891
R1			0.952
R2			0.939
R3			0.968
R4			0.969
R5			0.940
TI1				0.949
TI2				0.960
TI3				0.978
TI4				0.971
MI1					0.973
MI2					0.929
MI3					0.950
MI4					0.924
MI5					0.556
MF1						0.596
MF2						0.973
MF3						0.924
MF4						0.965
MF5						0.973

Table 6. The average variance extracted (AVE).

Measurement	AVE
Sense	0.938
Sensemaking	0.894
Response	0.910
Technological innovation	0.931
Marketing innovation	0.775
Marketing performance	0.807

Table 7. Cross loadings.

	Sense	Sensemaking	Response	Technological innovation	Marketing innovation	Marketing performance
S1	0.982	0.437	0.394	0.495	0.377	0.298
S2	0.956	0.343	0.374	0.447	0.320	0.258
S3	0.972	0.408	0.391	0.491	0.305	0.260
S4	0.962	0.376	0.311	0.430	0.337	0.257
SM1	0.413	0.967	0.296	0.385	0.319	0.187
SM2	0.419	0.972	0.297	0.394	0.330	0.192
SM3	0.403	0.949	0.288	0.383	0.269	0.151
SM4	0.301	0.891	0.350	0.423	0.328	0.207
R1	0.386	0.333	0.952	0.531	0.310	0.154
R2	0.319	0.273	0.939	0.514	0.263	0.177
R3	0.363	0.329	0.968	0.542	0.320	0.172
R4	0.389	0.328	0.969	0.549	0.331	0.191
R5	0.354	0.294	0.940	0.516	0.334	0.147
TI1	0.488	0.427	0.558	0.949	0.182	0.379
TI2	0.457	0.414	0.522	0.960	0.163	0.363
TI3	0.465	0.391	0.535	0.978	0.207	0.426
TI4	0.449	0.392	0.531	0.971	0.212	0.410
MI1	0.358	0.369	0.355	0.206	0.973	0.433
MI2	0.373	0.340	0.336	0.172	0.929	0.448
MI3	0.305	0.315	0.339	0.216	0.950	0.454
MI4	0.294	0.269	0.274	0.186	0.924	0.465
MI5	0.154	0.113	0.072	0.062	0.556	0.411
MF1	0.205	0.123	0.015	0.149	0.512	0.596
MF2	0.273	0.178	0.203	0.426	0.442	0.973
MF3	0.214	0.153	0.136	0.351	0.400	0.924
MF4	0.270	0.197	0.205	0.421	0.449	0.965
MF5	0.269	0.215	0.202	0.445	0.440	0.973

Table 8. Fornell-Larcker.

	Sense	Sensemaking	Response	Technological innovation	Marketing innovation	Marketing performance
Sense	0.968
Sensemaking	0.405	0.945
Response	0.380	0.327	0.954
Technological innovation	0.482	0.421	0.556	0.965
Marketing innovation	0.346	0.331	0.327	0.198	0.880
Marketing performance	0.278	0.196	0.176	0.409	0.499	0.898

Table 9. Composite Reliability and Cronbach’s Alpha.

Construct	Composite Reliability	Cronbach’s Alpha
Marketing innovation	0.943	0.918
Marketing performance	0.953	0.932
Response	0.981	0.975
Sense	0.984	0.978
Sensemaking	0.971	0.960
Technological innovation	0.982	0.975

Table 10. R square.

Dependent Variable	R Square
Technological innovation	0.423
Marketing innovation	0.193
Marketing performance	0.349

Figure 3. Inner model.

Table 11. Path coefficients.

Hypothesis	Original sample (O)	Sample mean (M)	T statistics (|O/STERR|)	P values
Sensing → Marketing innovation	0.197	0.204	1.987	0.047
Sensing → Technological innovation	0.255	0.247	3.205	0.001
Sense-Making → Marketing innovation	0.189	0.199	2.013	0.045
Sense-Making → Technological innovation	0.187	0.184	2.449	0.015
Response → Marketing Innovation	0.190	0.194	2.018	0.044
Response → Technological Innovation	0.398	0.404	4.363	0.000
Marketing Innovation → Marketing performance	0.435	0.440	5.850	0.000
Technological Innovation → Marketing performance	0.323	0.323	4.659	0.000

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