Inter-firm relational adaptation: analysis of dynamics from an international perspective

Figures & data

Figure 1. Effect of supplier asset specificity on relationship between adaptability and buyer asset specificity.

Figure 2. Effect of technological uncertainty on relationship between adaptability and buyer asset specificity.

Figure 3. Effect of volume uncertainty on relationship between adaptability and buyer asset specificity. NB: The interaction term was not significant in Tanzania.

Table

CONSTRUCTS	ITEMS	SOURCES	TZ LOADINGS	PL LOADINGS
BUYER ASSET SPECIFICITY αTZ= 0.68, αPL=0.84 CRTZ=0.82, CRPL=0.90 AVETZ=0.61, AVEPL=0.76	We have made significant investments in equipment dedicated to our relationship with this supplier	Stump and Heide (1996)	0.793	0.869
	We have made adjustments in order to deal with this supplier		0.787	0.897
	Training our people to deal with this supplier has involved substantial commitments of time and money		0.764	0.845
SUPPLIER ASSET SPECIFICITY αTZ= 0.88 αPL=0.83 CRTZ=0.94, CRPL=0.92 AVETZ=0.89, AVEPL=0.85	Supplier production system has been tailored to produce for our firm		0.944	0.924
	Supplier has customized the product we purchase from him to meet our specific needs.		0.944	0.924
TECHNOLOGICAL UNCERTAINTY (TECHUNC) αTZ= .82, αPL=.78 CRTZ=0.91, CRPL=0.89 AVETZ=0.7, AVEPL=0.67	The technology used in this product changes fast	Buvik and John (2000), Anderson (1985)	0.802	0.734
	The technology used in manufacturing this product is complex		0.902	0.872
	There is much R&D involved in the development of this product		0.868	0.886
VOLUME UNCERTAINTY (VOLUNC) αTZ= .060, αPL=0.82 CRTZ=0.79, CRPL=0.92 AVETZ=0.66, AVEPL=0.85	Demand for this product varies continually	Buvik and John (2000), Anderson (1985)	0.866	0.916
	The demand conditions for our supplier’s product(s) are irregular		0.814	0.922
	We are confident that the relationship will not break down		0.766	0.922
	We are confident that we will not lose our assets in this relationship		0.706	0.916
	We are confident that the supplier will adapt if circumstances change		0.732	0.870
	We have confidence that the supplier will meet the standards of our customers		0.785	0.889
ADAPTABILITY (ADAPT) αTZ= 0.67, αPL=.089 CRTZ=0.79, CRPL= 0.84 AVETZ= 0.55 AVEPL= 0.65	Arbitration procedures are well specified in our contract	Luo (2002), Aubert et al. (2006) Hendrikse and Windsperger (2011)	0.791	0.809
	Renegotiation periods were planned before the relationship began		0.705	0.885
	The contract specifies major principles or guidelines for handling unanticipated contingencies as they arise		0.830	0.884
	Finding another buyer would not affect the price this supplier charge		0.904	0.834
	If the relationship is terminated, it will not hurt this supplier		0.680	0.841

α_TZ, α_PL: alpha in the Tanzanian and Polish data; CR_TZ, CR_PL: composite reliability (Tanzanian/Polish data); AVE_TZ, AVE_PL: average variance extracted (Tanzanian/Polish).

Table 1. Fit indices for invariance tests.

Model	X^2	Df	X^2/df	RMSEA (90%CI)	TIL	CFI	Decision
Model 1: (Full configural)	621.2	298	2.08	0.052	0.89	0.913	Reject
Model 2: Partial Configural model	386.7	256	1.51	0.036	0.95	0.965	Accept
Model 3: Full Metric Model	455.7	269	1.69	0.042	0.93	0.95	Accept
Model 4: Full scalar model	774.9	271	2.85	0.068	0.809	0.864	Reject
Model 5: Partial scalar model	531.3	243	2.2	0.055	0.88	0.92	Accept
Model 6: Full Factor covariance	768.7	263	2.9	0.069	0.80	0.86	Reject
Model 7: Partial Factor covariance model	342.4	242	1.4	0.32	0.96	0.97	Accept
Model 8: Full Factor variance model	623.3	262	2.4	0.059	0.86	0.902	Reject
Model 9: Partial factor variance model	423.4	263	3.9	0.039	0.94	0.96	Accept
Model 10: Full error variance	623.3	262	2.4	0.059	0.86	0.902	Reject
Model 11: Partial error invariance model	350	251	1.4	0.039	0.96	0.97	Accept

Table 2. Correlation Matrix-Tanzania.

	1	2	3	4	5	6	7	8	9	10
1. ADAPTb	.81
2. BUASPb	0.220**	0.87
3. SUASPb	0.219**	0.379**	0.92
4. TECHNUNCb	0.371**	0.233**	0.197**	0.82
5. VOLUNCb	0.142*	0.006	−0.001	0.410**	0.92
6. BUASPXSUASPb	−0.021	0.245**	0.034	0.102	0.106	0.114
7. BUASPXTECHUNC	−0.016	0.378**	0.099	−0.057	0.080	0.021	na
8. BUASPXVOLUNC	0.139*	0.247**	0.105	0.080	−0.056	0.028	0.625**	na
9. SIZE	0.057	−0.037	−0.034	0.095	−0.090	−0.016	−0.045	0.056	na
10. LENGTH	0.036	0.054	−0.039	−0.012	−0.034	0.141*	0.009	0.027	−0.086	na
MEAN	3.172	1.939	2.490	3.255	3.609	4.048	0.232	−0.003	255.433	8.886
SD	1.060	1.006	1.238	0.837	0.810	0.695	1.074	1.049	1444.828	5.796

The elements in bold are the average variance extracted for constructs measured reflectively with multiple items, whereas the off diagonal elements are the square of correlations.  

Table 3. Correlation Matrix-Poland.

	1	2	3	4	5	6	7	8	9	10
1. ADAPT	0.74
2. BUASP	0.386**	0.78
3. SUASP	0.021	0.112	0.94
4. TECHNUNC	0.183**	0.281**	−0.264**	0.84
5. VOLUNC	0.312**	0.303**	−0.343**	0.346**	0.81
6. BUASXSUASP	−0.384**	−0.152*	0.181**	−0.001	−0.230**	na
7. BUASPXTECHUNC	−0.077	0.000	−0.003	0.001	−.0296**	−0.011	na
8. BUASPXVOLUNC	−0.105	−.355**	−0.153*	−0.170**	−0.116	−0.097	.220**	na
9. SIZE	−0.031	0.024	0.058	−0.135*	0.037	0.011	−0.078	0.009	na
10. LENGTH	−0.152*	−0.135*	0.093	−0.149*	−0.009	0.185**	−0.026	0.049	0.066	na
MEAN	4.249	3.978	3.594	3.374	3.679	0.114	0.272	0.308	1965.400	7.938
SD	0.588	0.683	1.239	1.050	0.825	0.940	0.763	1.297	14906.711	4.456

The elements in bold are the average variance extracted for constructs measured reflectively with multiple items, whereas the off diagonal elements are the square of correlations.  

Table 4. Regression results.

	POLAND						TANZANIA
INDEPENDENT VARIABLES	MODEL1		MODEL2		MODEL3		MODEL1		MODEL2		MODEL3
	β	t	B	T	B	T	β	t	β	T	β	t
Main effects
BUASP	0.14	2.11**	0.21	2.84***	.23	3.06***	0.21	3.02***	0.18	2.62***	0.17	2.37**
SUASP	0.10	1.5*	0.09	1.3	0.12	1.70**	0.89	0.374	0.11	1.80***	0.10	1.51**
TECHUNC	0.31	3.39***	0.19	2.57**	0.18	2.45**	−0.01	−0.20	0.05	0.74	0.05	0.61
VOLUNC	−0.07	−0.98	−0.02	−0.24	−0.04	−0.54	0.14	1.90**	0.08	1.05	0.14	1.84***
Interactive effects
BUASPXSUASP (H1a, b)			−0.11	−1.67**	−0.12	−1.88**			−0.32	−5.47***	−0.31	−5.21***
BUASPXTECHUNC (H2a, b)			−0.18	−2.05**	−0.21	−2.35**			−0.05	−0.87	−0.06	−1.05
BUASPXVOLUNC (H3a, b)			0.18	2.19**	0.17	2.16**			0.02	0.28	0.04	−0.68
Controls
SIZE					0.07	1.07					−0.06	−1.00
LENGH					−0.03	−0.45					−0.05	−0.82
R^2	0.27		0.30		0.33		.022		0.32		0.35
Adj.R^2	0.25		0.27		0.28		0.21		0.29		0.31
F-value	13.9***		10.20***		7.51***		13.5***		13.4***		9.97***
Incremental R^2	–		0.02**		0.01		–		0.08***		0.02**
F1	–		2.8**		0.91		–		9.00***		0.92
Maximum VIF	2.4		2.2		2.13		2.0		2.4		2.4

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

Table 5. Z-scores and comparison of regression weights.

Predictor	Poland		Tanzania
	b	SEb	b	SEb	SEb-difference	Z	p-Value
BUASP	0.23	0.08	0.16	0.06	0.10	0.47	p > 0.05
SUASP	0.12	0.06	0.10	0.03	0.07	0.12	P > 0.05
TECHUNC	0.18	0.10	0.04	0.06	0.12	1.12	p > 0.05
VOLUNC	−0.03	0.80	0.04	0.12	0.81	−0.12	p > 0.05
BUASPXSUASP	−0.12	0.07	−0.31	0.04	0.08	2.40	p < 0.05
BUASPXTECHUNC	−0.22	0.09	−0.07	0.05	0.10	−1.5	p < 0.05
BUASPXVOLUNC	0.18	0.08	0.04	0.03	0.09	0.61	p > 0.05

Table 6. Size effects comparison.

	COUNTRY	Mean	Std. Deviation	t	df	d*	F	r**	p
ADAPT	POLAND	3.17	1.07	−13.4	439	−1.25	58.91	−0.53	P < 0.01
	TANZANIA	4.25	0.59
BUASPb	POLAND	1.94	1.01	−25.2	439	−2.37	37.69	−0.76	P < 0.01
	TANZANIA	3.98	0.68
SUASPb	POLAND	2.49	1.24	−9.3	438	−0.89	0.006	−0.41	p > 0.05
	TANZANIA	3.59	1.24
TECHNUNCb	POLAND	3.26	0.84	−1.29	439	−0.12	26.69	−0.06	p > 0.05
	TANZANIA	3.37	1.05
VOLUNCb	POLAND	3.61	0.81	−0.89	439	0.09	0.00	−0.04	p > 0.05
	TANZANIA	3.68	0.83
	TANZANIA	4.03	0.62

* Is the cohen d distance **is the effect size (range between 0 and 1).

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