Keywords

Relationship Selling, Banking, Customer Satisfaction.

Introduction

The word relationship has now become one of the crucial aspects for the organization to serve their customers at their best. Customers prefer to engage in a close relationship with their current service provider. For an organization and specially all the financial service providers it is necessary to maintain a long-term relationship selling. There are various factors which leads to effective relationship selling and then these factors impact in building long term relations. It would be profitable for the organization to create close, personal and long-term relationships with all consumers.

In this research, the model which depicts antecedents impacts relationship selling are as customer orientation, customer knowledge, expertise, skills and empathy; and the consequences of this would lead to impact the purchase intention, word of mouth and the guilt proneness.

Research Objectives

To study the impact of factors leading to relationship selling.

Literature Review

Relationship Quality

Satisfaction with the relationship is considered as one of the significant aspects of buyer–seller relationships Liang & Wang (2006). Customer satisfaction still symbolizes a groundwork for customer-oriented business processes across a large group of companies operating in differential industries Szymanski & Henard (2001) and can be regarded as the crux of success in a cut throat competitive business world (Jamal & Naser, 2002). According to Liang and Wang (2006); Shekhar & Gupta (2008) suggested that trust is one of the main determinants which play a crucial role in persuading a customer to develop and maintain relationship with the service provider. Ndubisi (2007) suggested that customer satisfaction and relationship quality are affected by some of the factors like customer orientation, communication and competence. Relationship quality communicates the consequence of customer orientation, ability, knowledge and information sharing to customer loyalty. Each of these factors contributed differently to relationship quality like trust and satisfaction Izogo (2016). Donavan et al. (2004); Hennig-Thurau (2004) emphasized that the companies who target their actions on fulfilling the needs of their customers in a customer-oriented way carry out the Izogo (2017) activities better than those firms who do not perform the activities in a customer-oriented way. The level of satisfaction is directly related with concerned marketing strategies and relationship quality Hennig-Thurau & Klee (1997).

Client Knowledge

Customer knowledge has evolved in the past two decades in research on the quality of services and on relationship marketing. In relationship marketing, customer knowledge has often been considered as a significant dimension of the force of the relationship between a service provider and its clients Paulin et al. (2000). Knowing the customer is an essential component of the buyer-seller relationship. It is a determining element of the quality of a sound and efficient relationship Blanchard et al. (2001) and it contributes to creating a unique and inimitable competitive advantage. Teas (1988) observes that knowing the client and understanding his/her situation influence the quality with the customer’s relation Tsui & O'reilly (1989).

Customer Orientation

Guenzi et al. (2016) demonstrated that salespeople who are eager to win customer faith should change their approach in their relationship cycle. Similarly, when customer has low importance about buying decisions, salespeople should build relationships based on trust which must integrate adaptive selling and customer orientation. On the other hand, when importance of buying is high, salespeople can only generate more trust by increasing customer orientation or reducing. Customer orientation is often regarded as an index of the quality of buyer-seller relationships Cheng et al. (2008). Brown et al. (2002) described customer orientation as a personality variable that casts the service seller’s inclination to satisfy the customer needs. The salespeople must understand customers’ needs, expectations, and concerns, which is the support system for customer-orientation approach Saxe & Weitz (1982).

Expertise

Expert systems are very beneficial to sales forces of service organizations. Bernstein (1992) emphasized that sales personnel Cano et al. (2004) relate to customer perceptions of overall service quality received or not Zeithaml et al. (1988). It has become more necessary to establish a sales force that can improve the level of customer satisfaction beyond what the competition can offer Tonidandel et al. (2012). The professional expertise on performance throws a side beyond competency, i.e., extraordinary performance. Leong et al. (1989); Szymanski and Churchill, 1990 explained that qualitatively varied explanatory and systematic knowledge are with high-performing salespeople than low-performing salespeople, especially in more intricate selling situations, specifically service sale Crosby & Stephens (1987). The motivation for studying experts and expertise systems from trying to understand, and distributing best practices to achieve, extraordinarily performance widely so it is more comprehensive in services Fulop & Campbell (2011). The decision-making, or conclusions, processes of human experts by modelling the human “thinking” process in terms of a set of computer-based rules is imitated by the expert system Ural (2007). A persuasive expert system is one that holds both the logical process and the resulting decisions of human experts. The three essential elements of an expert system are a suitable knowledge base, an inference engine and a user interface Crosby et al. (1990).

Skills Required for Relationship Selling

The literature often discusses both traits and skills interchangeably. According to Yukl (2002), the term trait refers to a variety of individual attributes, including aspects of personality, temperament Winter (2000) needs, motives, and values, while the term skill refers to the ability to do something in an effective manner Turton et al. (2008). Managerial skills are defined as a set of integrated complementary skills possessed by the organization’s top management team. For the successful functioning of an intricate organization an individual requires all the managerial skills howsoever brilliant he is Carmeli & Tishler (2006). An effective top management team needs to have all the necessary skills being lined up with organization’s policies, goals, strategies and objectives, taking into consideration both macro and micro environmental factors Hunt (1991). Winter’s view (2000, 2003) was that top management of an organization is considered to be an asset, so it needs to possess complementary managerial skills, which evolve over time, are highly patterned; specific to the organization’s needs, and founded upon implicit knowledge Mann (1965).

Research Methodology

Marketing research is the scientific process of gathering and analyzing of marketing information to meet the needs of marketing management Winter (2003).

Research Design

In my report I am using descriptive research design. It is undertaken when the researcher wants to know the characteristics of certain group. It can be used to identify and classify the element or characteristics of the subject. Like with the help of this report I know the impact of relationship selling in the banking sector.

Sampling Methodology

➢ Target population: Sales persons working in the banks.

➢ Sampling method: Convenience sampling method.

➢ Sample size: 85 responses

Methods of Data Collection

In my research the data collection is primary data which is mainly done through the questionnaires which was administered by going to the banks getting personally filled by the sales persons Anders Ericsson (2008). The questionnaire was basically prepared based on the Likert scale technique Deaux & Major (1987).

Limitations

In spite the careful study in the project conduced, there are always some shortcomings, which can cause disturbances in the proper functioning of the project. Here are a few disturbances which can arise Ahamed & Skallerud (2015) Table 1.

Table 1 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy		0.652
Bartlett's Test of Sphericity	Approx. Chi-Square	3006.840
	Df	1128
	Sig.	0.000

➢ Due to time constraint, I have only considered Delhi region.

➢ The findings of the study assume that the respondents have given correct information.

Data Analysis & Interpretation

Interpretation

KMO and Bartlett’s test represents that whether it is suitable to do factor analysis or not. If the KMO value is more than 0.5 it suitable to go for factor analysis. Here it is 0.652 which is fine and hence factor analysis can be done for this study Table 2.

Table 2 Total Variance Explained
Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	13.034	27.153	27.153	13.034	27.153	27.153	5.472	11.400	11.400
2	3.389	7.061	34.214	3.389	7.061	34.214	3.946	8.221	19.622
3	3.157	6.577	40.791	3.157	6.577	40.791	3.659	7.623	27.245
4	2.719	5.664	46.455	2.719	5.664	46.455	3.634	7.571	34.816
5	2.451	5.106	51.561	2.451	5.106	51.561	3.155	6.574	41.390
6	2.075	4.324	55.885	2.075	4.324	55.885	2.757	5.744	47.134
7	1.866	3.888	59.773	1.866	3.888	59.773	2.228	4.641	51.775
8	1.536	3.201	62.974	1.536	3.201	62.974	2.173	4.528	56.303
9	1.421	2.961	65.935	1.421	2.961	65.935	2.126	4.430	60.733
10	1.347	2.807	68.741	1.347	2.807	68.741	1.962	4.088	64.821
11	1.246	2.596	71.338	1.246	2.596	71.338	1.912	3.982	68.803
12	1.113	2.318	73.656	1.113	2.318	73.656	1.618	3.370	72.173
13	1.046	2.180	75.836	1.046	2.180	75.836	1.548	3.225	75.398
14	1.025	2.136	77.972	1.025	2.136	77.972	1.236	2.575	77.972
15	0.993	2.069	80.041	-	-	-	-	-	-
16	0.877	1.827	81.868	-	-	-	-	-	-
17	0.769	1.602	83.470	-	-	-	-	-	-
18	0.737	1.535	85.006	-	-	-	-	-	-
19	0.724	1.509	86.515	-	-	-	-	-	-
20	0.573	1.193	87.708	-	-	-	-	-	-
21	0.552	1.151	88.858	-	-	-	-	-	-
22	0.541	1.128	89.986	-	-	-	-	-	-
23	0.509	1.060	91.046	-	-	-	-	-	-
24	0.450	0.938	91.984	-	-	-	-	-	-
25	0.391	0.815	92.799	-	-	-	-	-	-
26	0.361	0.752	93.551	-	-	-	-	-	-
27	0.312	0.651	94.202	-	-	-	-	-	-
28	0.283	0.591	94.792	-	-	-	-	-	-
29	0.274	0.571	95.363	-	-	-	-	-	-
30	0.266	0.554	95.917	-	-	-	-	-	-
31	0.230	0.478	96.395	-	-	-	-	-	-
32	0.202	0.421	96.816	-	-	-	-	-	-
33	0.196	0.408	97.224	-	-	-	-	-	-
34	0.163	0.340	97.563	-	-	-	-	-	-
35	0.156	0.326	97.889	-	-	-	-	-	-
36	0.147	0.307	98.196	-	-	-	-	-	-
37	0.138	0.288	98.484	-	-	-	-	-	-
38	0.123	0.256	98.740	-	-	-	-	-	-
39	0.110	0.229	98.969	-	-	-	-	-	-
40	0.091	0.190	99.159	-	-	-	-	-	-
41	0.085	0.177	99.335	-	-	-	-	-	-
42	0.077	0.161	99.496	-	-	-	-	-	-
43	0.067	0.140	99.637	-	-	-	-	-	-
44	0.057	0.119	99.756	-	-	-	-	-	-
45	0.039	0.081	99.837	-	-	-	-	-	-
46	0.033	0.070	99.907	-	-	-	-	-	-
47	0.027	0.056	99.963	-	-	-	-	-	-
48	0.018	0.037	100.000	-	-	-	-	-	-

Interpretation

In this, the number of factors extracted is determined so that the cumulative percentage of variance extracted by the factors reaches a satisfactory level Rich & Smith (2000). Here out of 48 factors only can be extracted further Petty et al. (1991) Figure 1.

Interpretation

A scree plot is a plot of the Bejou et al. (1996) Eigen values against the number of factors in order of extraction Scullen et al. (2003). The plot has a distinct break between the steep slope of factors, with large eigen values and a gradual trailing Smith (1998) off associated with the rest of the factors. The point at which the scree begins denotes the true number of factors Dwyer et al. (1987) Table 3.

Table 3 Component Matrixa
	Component
	1	2	3	4	5	6	7	8	9	10	11	12	13	14
RQ1	0.404	0-.205	0.014	0.073	0-.080	0.282	0-.138	0-.351	0.087	0-.165	0.487	0-.019	0-.100	0.303
RQ2	0.455	-0.009	0.233	0.092	-0.042	0.317	0.135	-0.423	0.048	0.051	0.014	0.236	-0.200	0.157
RQ3	0.353	-0.242	-0.127	0.198	-0.060	0.056	0.186	-0.491	0.192	-0.035	0.053	0.223	0.211	0.054
RQ4	0.593	-0.033	-0.011	0.155	-0.109	0.015	0.038	-0.278	-0.427	-0.246	0.073	-0.182	-0.037	0.006
S1	0.622	0.001	0.486	-0.149	0.067	0.037	-0.036	-0.123	-0.250	0.001	0.044	0.025	0.075	0.061
S2	0.597	-0.103	0.406	-0.056	0.416	0.001	0.070	-0.144	-0.161	0.151	-0.020	-0.016	0.078	0.011
S3	0.243	0.080	0.265	-0.083	0.453	-0.353	0.225	-0.186	-0.162	0.402	0.066	-0.267	-0.186	-0.008
CK1	0.525	-0.070	0.574	-0.147	0.192	0.171	-0.227	-0.052	0.170	-0.128	-0.071	0.064	0.015	-0.113
CK2	0.487	-0.199	0.373	0.034	0.445	0.257	-0.236	0.062	0.023	0.094	-0.216	0.040	-0.002	-0.142
CK3	0.436	-0.251	0.163	0.288	0.299	0.258	-0.333	0.057	0.094	0.210	-0.208	0.211	0.012	-0.193
CO1	0.747	0.015	0.251	-0.121	0.052	-0.148	-0.210	0.008	0.013	-0.204	0.007	-0.191	0.145	0.074
CO2	0.757	-0.054	0.191	-0.099	-0.023	0.037	-0.264	0.040	0.167	-0.098	0.171	-0.005	0.012	-0.004
E1	0.443	-0.241	-0.036	0.393	0.380	-0.001	0.225	0.090	0.321	-0.136	-0.055	-0.280	-0.093	0.135
E2	0.226	-0.315	-0.080	0.240	0.468	-0.041	0.160	0.146	0.364	-0.178	0.043	-0.093	-0.230	0.074
SK1	0.488	-0.212	0.096	-0.223	-0.259	0.291	0.362	0.290	-0.060	0.029	0.083	-0.133	-0.010	-0.134
SK2	0.719	-0.077	0.070	-0.036	-0.321	0.227	0.269	0.038	0.055	0.067	0.085	-0.123	-0.157	0-.067
SK3	0.400	-0.281	0.184	0.037	-0.368	0.241	0.365	0.272	-0.054	0.059	0.071	0.063	-0.218	-0.139
SK4	0.564	-0.387	0.035	-0.044	-0.201	0.225	0.211	0.234	-0.134	-0.032	0.192	0.031	0.174	-0.233
SK5	0.523	-0.444	-0.042	0.328	-0.205	-0.167	0.076	0.042	-0.125	0.009	0.042	0.029	-0.093	0.094
SK6	0.458	-0.391	-0.060	0.317	-0.256	-0.432	-0.164	-0.019	-0.017	0.110	0.034	0.187	0.091	-0.024
SK7	0.646	-0.298	0.065	0.172	-0.081	-0.428	-0.052	0.056	-0.176	0.010	0.026	0.024	-0.082	-0.039
SK8	0.636	-0.416	-0.089	0.138	-0.080	-0.388	-0.117	0.105	-0.095	-0.016	0.105	0.152	0.124	0.066
NV1	0.356	0.158	-0.251	-0.024	0.195	-0.249	-0.125	0.070	0.271	0.086	0.462	-0.092	0.010	-0.399
NV2	0-.201	-0.103	0.052	-0.090	0.490	-0.135	0.069	0.284	-0.054	-0.216	0.351	0.000	0.346	0.216
NV3	0-.585	-0.135	0.217	-0.220	0.199	0.051	0.273	0.092	0.042	0.000	0.134	0.230	0.288	0.070
NV4	0.360	0.225	-0.414	0.158	0.182	-0.220	0.408	-0.308	0.044	0.247	0.027	0.083	0.051	-0.116
PE1	0.702	0.340	-0.099	-0.203	0.006	0.110	-0.005	-0.138	-0.127	-0.344	0.011	-0.048	0.160	-0.111
PE2	0.543	0.339	-0.358	0.093	0.303	0.162	-0.017	0.011	-0.351	0.100	0.129	-0.014	0.014	-0.134
PE3	0.453	0.224	-0.354	-0.066	0.328	0.260	-0.147	0.105	-0.430	0.077	0.111	0.041	-0.028	-0.049
EC1	0.332	0.546	0.309	-0.008	0.037	-.119	0.110	0.144	0.160	-0.212	0.077	0.416	-0.183	-0.067
EC2	0-.072	-0.105	-0.019	-0.064	0.139	0.423	0.319	0.088	0.138	0.404	0.098	0.060	0.280	0.166
EC3	0.330	0.539	0.037	0.056	0.225	-0.151	0.255	0.152	0.008	-0.164	0.161	0.315	-0.333	-0.014
AS1	0.528	0.244	-0.324	-0.379	-0.115	0.041	-0.080	-0.110	0.158	0.096	-0.023	0.158	-0.015	0.109
AS2	0.578	0.272	-0.051	-0.386	-0.203	-0.074	-0.092	0.199	0.089	0.113	0.021	-0.012	0.092	0.269
AS3	0.671	0.333	-0.122	-0.146	-0.189	-0.105	-0.124	0.145	0.211	0.231	0.124	-0.056	0.045	0.093
AS4	0.449	0.048	-0.035	-0.042	0.127	-0.051	0.081	0.371	-0.240	0.030	-.210	0.097	-0.177	0.484
RP1	0.727	0.147	0.194	-0.205	0-.150	0.032	-0.211	0.054	0.116	0.043	0.001	0.001	-0.015	-0.041
RP2	0.759	0.067	-0.073	-0.060	-0.171	-.059	-0.183	0.049	0.195	0.174	-0.052	-0.180	0.021	0.072
RP3	0.577	-.089	-0.272	0.143	0.019	0.052	-0.240	0.075	0.022	0.383	-0.012	0.107	-0.044	0.089
OP1	0.691	0.162	0.148	-0.251	-0.004	-0.189	0.280	-0.149	0.152	-0.051	-0.183	-0.184	0.093	0.024
OP2	0.589	0.113	0.268	-0.305	-0.063	-0.240	0.317	-0.112	-0.010	0.014	-0.244	-0.074	0.097	-0.018
PI1	0.686	-.006	-0.270	0.067	0.039	-0.094	0.132	0.008	-0.054	-0.084	-0.228	0.308	0.216	0.037
PI2	0.610	0-.149	-0.324	0.084	0.081	-0.078	0.197	0.130	0.076	-0.258	-0.278	0.045	0.158	-0.167
W1	0.477	0.131	-0.525	-0.053	0.056	0.244	0.003	-0.037	0.114	-0.136	-0.228	-0.059	0.014	-0.065
W2	0.534	-.016	-0.537	0.167	0.143	0.339	-0.075	0.027	-0.001	-0.158	-0.045	-0.148	-0.028	0.143
G1	0.161	0.552	0.242	0.621	-0.147	0.088	0.053	0.019	0.028	0.076	0.145	-0.013	0.199	0.010
G2	0.067	0.491	0.249	0.678	-0.142	0.035	0.116	0.119	0.065	0.090	-0.057	-0.194	0.196	0.060
G3	0.031	0.469	0.323	0.623	-0.174	0.107	-0.093	0.116	-0.080	-0.073	-0.073	-0.025	0.104	0.032

Interpretation

Component matrix or factor pattern represent how the factors are correlated with the variables. Having higher correlation between the factors and variables, classify those together Rajivan et al. (2017) Table 4.

Table 4 Rotated Component Matrixa
	Component
	1	2	3	4	5	6	7	8	9	10	11	12	13	14
RQ1	0.189	0.137	0.093	0.119	0.124	-0.033	-0.133	0.144	-0.078	0.811	-0.059	0.078	0.062	0.065
RQ2	0.083	0.028	.297	0.040	0.168	0.074	0.151	0.009	0.137	0.605	0.195	-0.095	-0.287	-0.161
RQ3	0.003	0.281	.054	-0.015	0.025	0.000	0.493	0.089	-0.012	0.527	-0.062	-0.157	-0.093	0.107
RQ4	0.085	0.279	.070	0.365	0.256	0.134	0.185	-0.026	0.207	0.360	-0.070	0.470	-0.043	-0.097
S1	0.290	0.152	.514	0.072	0.246	0.070	0.070	-0.181	0.341	0.253	0.096	0.182	0.130	-0.155
S2	0.142	0.154	.594	0.146	0.134	0.007	0.143	0.065	0.521	0.163	0.053	-0.007	0.142	-0.082
S3	0.060	0.050	.140	0.060	-0.062	-0.035	-0.073	0.113	0.890	-0.044	0.082	-0.018	0.011	0.052
CK1	0.274	0.019	0.789	-0.123	0.148	0.003	0.050	0.053	0.063	0.172	0.131	0.162	0.038	0.024
CK2	0.065	0.069	0.848	.160	0.090	-0.012	0.011	0.193	0.119	-0.012	-0.014	-0.041	-0.026	-0.051
CK3	-0.023	0.283	0.745	.208	0.007	0.082	0.011	0.177	-0.094	-0.001	-0.036	-0.206	-0.198	0.048
CO1	0.561	0.240	0.415	.078	0.116	0.069	0.136	0.109	0.153	0.122	-0.004	0.373	0.180	-0.012
CO2	0.548	0.259	0.450	.097	0.214	0.001	0.000	0.115	-0.027	0.249	0.101	0.173	0.049	0.134
E1	0.046	0.178	0.173	.080	0.113	0.143	0.158	0.824	0.147	0.105	-0.036	0.003	0.048	-0.016
E2	0-.085	0.134	0.149	.020	0.026	-0.117	0.022	0.786	0.042	0.059	0.122	-0.044	0.094	0.058
SK1	0.247	0.027	0.088	.076	0.813	-0.076	0.072	0.046	0.042	0.010	-0.031	-0.033	0.013	-0.042
SK2	0.395	0.149	0.101	.116	0.669	0.071	0.110	0.125	0.112	0.244	0.054	0.034	-0.243	0.007
SK3	0.036	0.204	0.076	0-.051	0.797	0.024	-0.036	0.057	-0.020	0.068	0.126	-0.067	-0.175	-0.093
SK4	0.112	0.317	0.208	0.164	0.738	-0.061	0.152	-0.005	-0.077	0.089	-0.077	-0.028	0.136	0.101
SK5	0.040	0.684	0.033	0.058	0.318	0.040	0.050	0.210	0.047	0.184	-0.063	0.076	-0.096	0-.118
SK6	0.124	0.858	0.068	-0.067	0.043	0.041	0.105	0.012	-0.019	0.066	-0.057	0.033	-0.097	0.091
SK7	0.192	0.734	0.167	0.067	0.195	-0.014	0.061	0.107	0.225	0.016	0.069	0.242	-0.033	-0.008
SK8	0.244	0.820	0.137	0.094	0.142	-0.099	0.127	0.104	0.021	0.076	-0.006	0.087	0.137	0.010
NV1	0.306	0.164	-0.005	0.251	-0.015	-0.043	-0.031	0.178	0.107	-0.057	0.185	0.019	0.083	0.723
NV2	-0.128	-0.038	-0.003	0.018	-0.130	-0.089	-0.075	0.159	0.036	-0.064	.036	-0.084	0.810	0.017
NV3	-0.364	-0.285	-0.050	-.348	-0.033	-0.164	0.014	-0.162	-0.017	-0.071	0.039	-0.347	0.470	-0.039
NV4	0.078	0.174	-0.249	0.341	-0.073	0.065	0.485	0.130	0.392	0.087	0.185	-0.223	-0.175	0.233
PE1	0.463	-0.058	0.179	0.434	0.180	0.057	0.415	-0.074	0.020	0.186	0.161	0.380	0.073	0.064
PE2	0.165	0.063	0.085	0.833	0.058	0.129	0.118	0.005	0.184	0.031	0.141	0.013	0.002	0.109
PE3	0.149	0.021	0.153	0.853	0.054	-0.054	-0.024	-0.057	0.070	0.009	0.097	0.000	0.066	-0.021
EC1	0.258	-0.055	0.175	-0.047	0.036	0.241	0.094	-0.043	0.004	0.010	0.811	0.098	0.017	0.026
EC2	-0.017	-0.222	0.029	0.046	0.211	0.018	0.054	0.037	0.052	0.096	-0.174	-0.676	0.162	-0.053
EC3	0.140	-0.033	-0.034	0.234	0.022	0.158	0.049	0.130	0.166	0.006	0.821	0.074	0.029	0.005
AS1	0.638	0.018	-0.022	0.281	-0.006	-0.225	0.227	-0.086	-0.047	0.153	0.167	-0.091	-0.182	0.018
AS2	0.816	0.083	0.009	0.132	0.130	-0.033	0.040	-0.098	0.031	-0.006	0.122	-0.028	0.060	-0.130
AS3	0.802	0.184	0.010	0.206	0.106	0.135	0.021	0.011	0.069	0.018	0.143	-0.074	-0.092	0.137
AS4	0.314	0.233	0.058	0.289	.103	-.018	-0.058	0.190	0.115	-0.102	0.229	-0.044	0.089	-0.609
RP1	0.646	0.146	0.383	0.095	.226	.046	0.041	0-.056	0.040	0.096	0.140	0.134	-0.119	0.054
RP2	0.714	0.283	0.181	0.169	.150	.082	0.082	0.144	0.074	0.060	-0.075	0.042	-0.197	0.057
RP3	0.360	0.438	0.186	0.405	.017	.005	-0.044	0.114	-0.005	0.067	-0.051	-0.268	-0.234	0.006
OP1	0.563	0.028	0.154	-0.037	.212	.014	0.465	0.133	0.392	0.060	0.103	0.182	-0.037	-0.040
OP2	0.445	0.074	0.178	-0.116	.263	-.024	0.437	-0.059	0.442	-0.037	0.134	0.161	-0.032	-0.135
PI1	0.282	0.418	0.134	0.341	.096	-.003	0.565	0.076	-0.045	0.024	0.179	-0.047	-0.008	-0.143
PI2	0.184	0.316	0.111	0.277	.233	-.065	0.576	0.342	-0.110	-0.142	0.052	0.119	-0.013	0.002
W1	0.319	-0.060	0.004	0.503	.062	-.090	0.391	0.250	-0.199	0.022	-0.033	0.045	-0.224	0.018
W2	0.239	0.080	0.017	0.647	.085	.005	0.176	0.416	-0.214	0.201	-0.134	0.043	-0.088	-0.077
G1	0.066	0.013	0.009	0.071	-.002	.896	0.019	-.051	0.024	0.117	0.175	-0.055	-0.016	0.104
G2	0.034	-0.018	-0.040	-0.026	-.006	.940	0.030	0.098	0.062	-0.065	0.030	-0.035	-0.053	-0.019
G3	-0.050	-0.006	0.101	0.011	-.045	.852	-0.068	-0.060	-0.112	-0.019	0.125	0.122	-0.063	0-.103

Interpretation

It is derived by multiplying factor pattern/ component matrix with orthogonal transformation matrix/ component transformation matrix. It is likely to better off the situation that is not necessarily true for all the situations Table 5.

Table 5 Component Transformation Matrix
Component	1	2	3	4	5	6	7	8	9	10	11	12	13	14
1	0.554	0.383	0.347	0.340	0.323	0.057	0.258	0.171	0.162	0.200	0.136	0.122	-0.085	-0.002
2	0.325	-0.458	-0.182	0.236	-0.270	0.494	0.042	-0.231	0.094	-0.122	0.430	0.085	-0.069	0.056
3	-0.051	-0.109	0.555	-0.569	0.171	0.291	-0.210	-0.179	0.288	0.076	0.141	0.127	0.144	-0.137
4	-0.415	0.375	-0.016	0.086	-0.110	0.716	-0.024	0.325	-0.106	0.098	-0.026	-0.039	-0.157	0.035
5	-0.243	-0.187	0.398	0.333	-0.376	-0.185	0.034	0.415	0.339	-0.097	0.140	-0.140	0.348	0.052
6	-0.116	-0.533	0.288	0.323	0.358	0.090	-0.077	0.002	-0.368	0.318	-0.187	-0.263	-0.131	-0.127
7	-0.229	-0.166	-0.390	-0.129	0.500	0.040	0.438	0.190	0.378	0.017	0.228	-0.231	0.084	-0.128
8	0.144	0.091	-0.005	0.069	0.344	0.096	-0.346	0.201	-0.300	-0.648	0.174	-0.139	0.313	-0.143
9	0.340	-0.175	0.050	-0.491	-0.145	-0.008	0.120	0.503	-0.264	0.067	0.087	-0.291	-0.146	0.365
10	0.135	0.134	0.029	0.058	-0.025	0.051	-0.257	-0.212	0.429	-0.157	-0.206	-0.697	-0.318	0.087
11	0.016	0.082	-0.235	0.092	0.169	0.011	-0.427	-0.065	0.055	0.460	0.163	-0.068	0.447	0.518
12	-0.187	0.276	0.164	-.0022	-0.126	-0.166	0.187	-0.340	-0.336	0.120	0.620	-0.378	-0.009	-0.110
13	0.118	0.048	0.088	-0.034	-0.077	0.265	0.490	-0.287	-0.134	-0.086	-0.417	-0.216	0.559	0.133
14	0.290	0.049	-0.229	-0.076	-0.267	0.048	-0.174	0.176	0.014	0.369	-0.065	-0.184	0.254	-0.694

Findings

From the component and rotated matrix table, the following table arises which shows that for in a factor, the number of variables is highly correlated with each other Table 6.

Table 6 Factors and Variables
FACTORS	NO. OF VARIABLES
1	AS1, AS2, AS3, RP1, RP2
2	SK5, SK6, SK7, SK8
3	CK1, CK2, CK3, S3
4	PE2, PE3, W1
5	SK1, SK2, SK3, SK4
6	G1, G2, G3
7	NV4, PI2
8	E1, E2, W2
9	S3
10	RQ1, RQ2, RQ3
11	EC2
12	EC11
13	NV2
14	NV1

Conclusion

This is an orthogonal method of rotation which minimizes the number of variables with high loadings on a factor, thereby enhancing the interpretability of the factors. Orthogonal rotation results in factors that are uncorrelated.

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Received: 22-Jun-2022, Manuscript No. AMSJ-22-12229; Editor assigned: 24-Jun-2022, PreQC No. AMSJ-22-12229(PQ); Reviewed: 08-Jul-2022, QC No. AMSJ-22-12229; Revised: 20-Jul-2022, Manuscript No. AMSJ-22-12229(R); Published: 27-Jul-2022