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Article

The Importance of Digital Signature in Sustainable Businesses: A Scale Development Study

by
Nadire Cavus
1,2,* and
Nuriye Sancar
2,3
1
Department of Computer Information Systems, Near East University, 99138 Nicosia, Turkey
2
Computer Information Systems Research and Technology Centre, Near East University, 99138 Nicosia, Turkey
3
Department of Mathematics, Near East University, 99138 Nicosia, Turkey
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(6), 5008; https://doi.org/10.3390/su15065008
Submission received: 19 November 2022 / Revised: 24 February 2023 / Accepted: 3 March 2023 / Published: 11 March 2023
Browse Figure
Versions Notes

Abstract

:
Today, the interest and the needs of all sectors for digital signatures are increasing day by day. The next approvals in businesses cannot take place without the previous approvals, which follow one after the other, such as a chain of transactions. For this reason, the approval (that is, the signature of a relevant unit) should be carried out as soon as a document is received and immediately forwarded to the next unit. Digital signatures are needed to carry out business transactions quickly and reliably. The use of digital platforms and systems, which are indispensable parts of digital transformation, can be achieved primarily with awareness and understanding of their importance. However, a digital signature awareness scale was not found in the literature. Therefore, in this study, a new scale was developed which can be used to determine the digital signature awareness of people and it is hoped to eliminate this deficiency in the current literature. The developed scale consists of two dimensions: awareness and benefits. The study was designed as descriptive research and 567 participants voluntarily participated in the study. Factor analysis and descriptive analysis methods were used to analyze the collected data. In line with the statistical analysis results, it has been observed that the developed scale provides validity and reliability features and is qualified to be used in scientific research to determine the awareness of people about digital signatures. With the digital signature scale, which is thought to fill the gap in the literature, it is hoped that the digital signatures awareness of people in developing countries, and in particular in Cyprus will be determined, and it is also hoped that people will be encouraged to use digital signatures after necessary training.

1. Introduction

Today, the concept of distance has disappeared as a result of developments in technology. The distances between countries have been eliminated as a result of the developments in information and communication technologies (ICT), software, and hardware. In the age of technology, it has become a necessity for business members to develop their digital skills in order for societies to realize digital transformation. Innovations brought by technology in education [1,2], communication [3], banking [4], finance [5], etc. affected and changed all sectors of the industry in sustainable business. It has digitized transactions in almost all sectors, causing them to move to more efficient online platforms [6]. Sustainable businesses are deploying new web videoconferencing systems, online platforms, electronic documents, etc. to compete with businesses all over the world and stay at the forefront by adopting and using new technologies in businesses [7]. Especially, in unexpected pandemic conditions such as the current COVID-19, individuals from all over the world have had to do all their activities/transactions online [8], from continuing their lives to running their businesses [6]. Individuals with insufficient digital competence courses had difficulties [9], but they made an effort to develop their digital skills out of necessity to exist in businesses.
As a result of digital transformation, people have started to communicate and collaborate easily and quickly with people elsewhere in the world [10]. This has increased the use of digital technology and led the world to go online. Increasingly, business deals are forcing business communications, official data, and business transactions to be conducted online [11]. At the same time, the functionality, capacity, and accessibility features of ICT support remote working [12] and thus allow the work to continue without interruption, even in pandemic conditions [6]. Because of this, there is a world transformation from paper-based work to digital-based work. However, timely signing and forwarding documents to the relevant departments are vital for employers to maintain their presence in competitive business environments [10]. Paper documents usually take time to reach businesses that are far apart, for example, businesses that are located in different countries or cities, and this can result in unnecessary delays in signing the relevant documents. As a result of these delays, businesses often experience undesirable consequences and may even suffer big losses. In the last few years, the need for digital signature usage has arisen for both the public and the business sector [13] and this has caused the awareness of the digital signature to increase among the sustainable business sectors. One can conclude that the popularity of digital signatures is increasing day by day [7,8,9,10,11,12,13,14], especially during the COVID-19 pandemic period [15]. At this point, digital signature studies have accelerated. However, nowadays, it is seen that digital signature systems are not used adequately or at the desired level [16]. There are different reasons for this. As well as the lack of technological equipment, it is possible to reach the desired goal in the quality use of digital signatures by determining the awareness level of individuals about digital signatures and by increasing the awareness of people by carrying out the necessary studies/training in line with these needs. However, unfortunately, when the literature is examined in detail, it is seen that sufficient scale cannot be reached in this area. For this reason, this study has been purposed to develop a scientific scale that can determine people’s awareness of digital signatures in businesses. Then, the developed scale was used to collect data from participants to understand their digital signature awareness.
The remaining parts of the article are arranged as follows: In Section 1.1, information about electronic documents and digital signatures is provided. Materials and research methods are explained in Section 2. Results are provided in Section 3. Finally, the discussion and conclusion are presented in Section 4 and Section 5, respectively.

1.1. Electronic Documents and Digital Signature

Documents can be important letters or reports usually signed by authorized officials. The authenticity of a digital document is indicated by the digital stamp attached to it. One of the major disadvantages of classical paper documents is that such documents can easily be lost or damaged, and handwritten signatures attached to such documents can easily be faked. Nowadays, most important documents, such as legal documents, are in the form of digital documents, also called e-documents [17].
Electronic documents (e-document) are easier to search, save space, have flexible characteristics, are easier to archive digitally, have better security features, are easier to transfer and restore, and consequently are replacing paper documents [18]. A mechanism is needed to protect the data in a digital document reliably [19]. Electronic document signing required an authentication mechanism and digital signature-based systems have thus been developed [20]. The authenticity of electronic documents can be maintained by attaching digital signatures to them [21]. Digital documents are signed using digital signatures similar to the process of signing handwritten signatures [22]. The number of paper documents such as files, letters, reports, and certificates to be signed is reduced by using an e-document system, usually signed by the responsible official in charge [23].
The digital signature market is growing at an unexpected rate with an estimate from $1.83 billion in 2019 to 2.33 billion in 2020, representing an annual growth rate of 27.69%, and the digital signature market is forecasted to be approximately $4.95 billion in 2023 at a compound annual growth rate of 28.58% [24]. Additionally, the report stressed that a lack of awareness about the legality of digital signatures has been one of the factors that slowed down the expansion of the market. Moreover, the report highlighted that it is however expected that the digital signature market will grow to $4.95 billion by the year 2023. As a result of the COVID-19 pandemic, individuals have been isolated and there have been restrictions on people’s movements [25]. Consequently, businesses and people prefer to go online when at their homes [9] and use electronic signatures as an alternative to classical handwritten signatures [26]. So, the demand for digital signature markets is increasing day by day [14]. The digital signature market consists of companies selling mainly digital signature solutions in sustainable businesses and related services in the form of software packages. Although there are many digital signature software packages available, the popular ones are DocuSign, Adobe Systems, SIGNIX, Ascertia, Kofax, Datacard, and RPost.

2. Related Research

The literature review showed that researchers focused on the factors that affect the digital signatures’ adaption/acceptance/intention. Aydin, Çam, and Alipour (2018) determined affecting factors to the use of digital signature systems by the technology acceptance model (TAM) [27]. The study results pointed out that “perceived usefulness and utilization ease” influenced consumers’ attitudes positively. Additionally, Santosa et al. (2022) determined factors that affect consumer intention in using digital signatures based on the Unified Theory of Acceptance and Use of Technology2, the Theory of Planned Behavior, and the Information Acceptance Model [15]. Notably, 358 participants attended the survey and the structural equation modeling technique was utilized to analyze collected data and to perform the test of hypotheses. The integrated model indicated that the correlation between consumers’ attitudes, perceived behavioral control, subjective norms, and information adoption was found in consumers’ behavioral intentions. However, the findings indicated that the attitudes of consumers had the most important effect on consumers’ attitudinal intention for the use of digital signatures. Moreover, Chong, Kim, and Choi (2012) highlighted the gap in the literature on the intention for digital signature acceptance [28]. For this reason, they investigated the factors affecting the intention of participants to adopt cloud-based digital signature services. The proposed model included a technology, organization, and environment framework. The study results showed that all characteristics contained by the model affect the participants’ intention to adopt digital signatures significantly.
On the other hand, some of the studies focus on other perspectives that affect digital signatures, such as the digital signature algorithm [29], the quantum signature method [30], digital signature standards [31], security [32], etc. For example, security is the most important issue for digital signatures. The digital signature algorithm supplies security in interpersonal messages in the approval process. For this purpose, many algorithms have been developed by researchers in the literature. Jalaja, Anjaneyulu, and Narendra Mohan (2022) created a novel digital signature scheme according to the Conjugacy problem of non-commutative rings [29]. Then, the power of the developed algorithm was examined by confirmation theorem and further security analysis has been performed. The confirmation theorem analysis results showed the power of the developed algorithm. Additionally, some researchers had proposed different kinds of signature methods. Huang, Xu, and Song (2023) developed a public-key quantum signature method according to identity [30]. The results of the study showed that the developed scheme was more efficient than available quantum digital signature protocols in the literature.
Additionally, Ribeiro, de Almeida, and Canedo (2021) examined the most important technologies and standards of digital signature used in the related studies to create a digital signature system model for the University of Brasília-UnB in line with MEC and ICP-Brazil standards [31]. The obtained comparative test results showed that the developed model can be used to develop subscription systems, especially for Brazilian Universities in the future. However, no scale development studies were encountered that could determine digital signature awareness in the literature. However, awareness of new technology’s benefits positively affects the adaption, acceptance, and use of that technology in society. For this reason, in this study, a scale that can determine digital signature awareness has been developed to fix the gap in the literature.

3. Materials and Methods

3.1. Research Design

This study, named AoDiG-sign, is designed as descriptive research that aims to develop the scale to determine the awareness of people among digital signatures. The stages of the AoDiG-sign scale development study and the characteristics of the participants are presented below.

3.2. Participants and Procedure for the Scale Development

A total of 567 students who had knowledge about digital signatures voluntarily participated in the study from universities in Northern Cyprus. Before starting the data analysis, multivariate extreme, outlier, missing, or erroneous values were checked and corrected. Firstly, missing data was investigated. Missing data points were observed in approximately 1% of subjects. These data were changed with the median value. Multivariate outlier values were evaluated using Mahalanobis distance (p < 0.001). Datapoints whose Mahalanobis distance values are greater than  χ 2   = 45.315 were determined as outliers and were removed from the dataset. After removing these values, 556 collected data were included in the evaluation.
In addition, 65.3% (n = 363) of the study group were male and 34.7% (n = 193) were female. Notably, 57.9% (n = 322) of the students were from the Faculty of Engineering and the remaining 42.1% (n = 234) were from the Faculty of Economics and Administrative Sciences. Notably, 30.2% (n = 168) of these students were from the Software Engineering Department, 16.2% (n = 91) were from Information System Engineering Department, 11.3% (n = 63) from Computer Engineering Department and 27.5% (n = 153) were from the Computer Information Systems Department and 14.6% (n = 81) from the Management Information Systems. Their ages varied from 18 to 35 years, and the average age of the students was 21.36. Participants were identified by convenience sampling. In addition, 370 of the participants were undergraduates, and 186 were at the graduate level. Students at the undergraduate level are also seniors. Therefore, they have sufficient knowledge about the technical structure of the digital signature we have mentioned. While determining the study group, students studying in technology-based departments are given courses on both the digitalization processes of businesses and digital transformation processes, and those who were volunteering to participate were taken into account as the inclusive criteria. Those who did not meet these characteristics were not included in the study. Since digital signature software is a technological tool, students in technology-based departments were included in the study. Therefore, students studying in technology-based departments are knowledgeable about the technological structures of digital signatures and the benefits as a result of the usage of digital signatures. Because they are aware of digital signatures structure and its benefits, they understood the questions in this questionnaire when they read it, and then they reflected their thoughts more consistently. As a result of this, response bias was prevented, which is a very important issue in a survey. Since we have collected consistent answers and thus the quality of the data has been high, the validity and reliability results used in the scale development phase will reflect the truth. The AoDiG-sign scale was conducted again on 100 random samples of participants to assess the test-retest reliability of the scale after two weeks of the initial questionnaire application.
In the first step of the study, the existing research was reviewed, and the indicators related to the concept of digital signature were investigated. In this context, studies carried out in the literature were analyzed and phrases that could be used in the scale were defined. The first 25-item pool was created, taking into account each sign related to the digital signatures. The content validity of these items was both qualitatively and quantitatively performed. In the qualitative step, the 25-item trial form was evaluated by 10 information technology (IT) experts, who were knowledgeable in the subject area and were informed about the study, to attain expert opinions. It has been shown that for the content validity analysis, the number of experts should be at least 6 and should not be more than 10 [33]. In the quantitative step, for each item, the content validity ratio (CVR) and content validity index (CVI) have been computed. A three-point ordinal rating scale (necessary and sufficient/useful but insufficient/not necessary) was used to calculate the CVR of each item. This ratio was calculated by taking the ratio of the number of experts who offered positive answers to each item to the number of experts, minus one. If CVR for each item is higher than Lawshe’s criteria, the item is valid; otherwise, it is eliminated. The critical CVR value for 10 experts is 0.62 according to Lawshe’s criteria [34]. Then, another four points ordinal scales for relevance, clarity, and simplicity were used to calculate the item content validity index (I-CVI) and scale content validity (S-CVI/Ave) (from 1: not relevant, not simple, not clear to 4: highly relevant, highly simple, highly clear) [35,36]. The I-CVI of each item is computed by dividing the number of experts rating 3 or 4 by the number of all experts in the study for every four points ordinal scale. S-CVI/Ave is calculated as the mean of all I-CVIs. I-CVI higher than or equal to 0.78 is acceptable, between 0.7 and 0.78 is required to be revised and less than 0.7 is unacceptable and has to be omitted [36,37]. S-CVI/Ave higher than or equal to 0.90 is acceptable. Average values of CVR and I-CVI for the total scale have been also calculated before and after removing the items. In line with the content validity rates obtained, 5 items were excluded from the scale. The scale was concluded according to the report maintained by these experts. This scale has no reversed items, and thus, a measuring scale consisting of 20 items was formed to IT experts’ opinions and recommendations for the development of the AoDiG-sign scale. This scale includes five-point Likert scale items (strongly disagree (1), disagree (2), neutral (3), agree (4), strongly agree (5)). A questionnaire was prepared including a demographic section (age group, gender, education level, faculty, nationality, and information regarding the use of digital signatures) and an AoDiG-sign scale.

3.3. Data Analysis

First, the normal distribution of the items was investigated by the value of Skewness and Kurtosis. Normality of items is assumed to occur when Skewness is between −2 and 2, and Kurtosis is between −7 and 7. The Skewness and Kurtosis values of the items were calculated ranging between −2 and 2 and −7 and 7, respectively. As a result of these values, the validity and reliability analysis of the scale were performed in line with the data collected from a total of 556 students participating in the research. When the scale development studies in the literature were examined, it has been seen that Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) studies could be applied to the data obtained by randomly dividing the same sample group into two. In this study, the group participating in the study was randomly divided into 2 subgroups (n1 = 278; n2 = 278) considering both time and financial possibilities. Exploratory Factor Analysis (EFA) was performed on the first group and Confirmatory Factor Analysis (CFA) was applied to the other group. Within the scope of the research, firstly, the content validity of the scale was checked. Afterward, Barlett Sphericity Test and Kaiser-Meyer Olkin (KMO) coefficient were evaluated whether the dataset was convenient for factor analysis and then EFA based on principal component analysis (PCA) with oblimin rotation was applied to examine the features for the construct validity of the scale. After the factor analysis processes, the entire scale, subfactors, and scale items in each subfactor were separately analyzed for reliability. In this process, for the assessment of the reliability analysis, the Sperman-Brown coefficient and Cronbach’s Alpha were evaluated as internal consistency scores. Pearson’s (r) and Intraclass (ICC) correlation coefficients were calculated for retest reliability. Additionally, convergent validity and discriminant validity were investigated with detailed calculations and comparisons. Furthermore, corrected item-total correlation and Cronbach’s Alpha (if the item was deleted) were calculated for item analysis, and then Student’s t-test was applied to control if the items of the scale discriminate between the lower and upper 27% of the participants. In addition, CFA was applied to check the accuracy of the factor structure obtained by EFA. All statistical analyses of the dataset were performed using Statistical Program for Social Science (SPSS) version 24 and R Studio version 4.1.1 for Windows software with foreign and Lavaan packages.

4. Results

4.1. Content Validity Index of the AoDiG-Sign Scale

CVR of all the items varied between 0.8 and 1.0 except for 5 items (items 3, 5, 10, 15, and 21) as seen in Table 1. CVR values of these 5 items were less than 0.62, and thus these items were removed from the scale. It was also observed that the I-CVI values of the same 5 items for relevance, clarity, and simplicity were lower than 0.70, and the I-CVI values of the remaining 20 items varied between 0.8 and 1, which are higher than 0.78. Before removing the improper 5 items, the mean CVI of relevance, simplicity, and clarity was 0.828, 0.788, and 0.788, respectively, and S-CVI/Ave was 0.801. After removing these improper items, mean CVI of relevance, simplicity, and clarity have increased to 0.99, 0.955, and 0.945, respectively, and S-CVI/Ave has increased to 0.963. On the other hand, the mean of CVR was 0.568 before removing these 5 improper items. After removing them, it increased to 0.86 > 0.62. These results indicated the improvement of the entire scale after removing these five items. In line with the content validity analysis, five items were excluded from the scale. Thus, as a result of these steps, a 20-item form was created.

4.2. Explanatory Factor Analysis of the AoDiG-Sign Scale

Explanatory Factor Analysis (EFA) using PCA with oblimin rotation was applied to investigate the factor structure of the scale. The factors of the scale were identified according to the criteria: eigenvalues which are greater than one and scree plot. The results of the loadings and communalities of the scale were determined for factorability for the 20 items. Bartlett’s test of sphericity and Kaiser’s Meyer Olkin (KMO) measure were calculated to ensure that the structure of the dataset was convenient for the factor analysis. As a result of Bartlett’s test of sphericity, the dataset is normally distributed and is suitable for the analysis (χ2 = 1793, df = 120, p < 0.001). KMO was evaluated as 0.890, demonstrating that the sample size is adequate for the analysis because the KMO value is higher than 0.80 [38]. The cutoff value of factor loadings was taken as 0.4 as in this study and values less than 0.4 were not utilized. The items whose communality is less than 0.2 were excluded from the study [39]. The item “Aged people will have difficulties by adapting digital signature because it’s new technology” was excluded since the factor loading was weak, at 0.312. Additionally, it is observed that more than three items possessed high factor loading values in two dimensions based on EFA results. These results were approved to remove these items with high loading values in two dimensions. The other 16 items were collected under two dimensions according to the number of eigenvalues that are greater than or equal to 1 as seen in Table 2. For the scale, the explained variance by these two dimensions was 50.49%. The eigenvalues indicated that 38.23% of the variance was accounted for by the first dimension, which was identified to be significant, and the second dimension accounted for 12.26% of the total variance. Likewise, the result in the scree plot indicated the scale has two-factor as seen in Figure 1. When the items in the scale are analyzed in the dimensions, respectively, the first dimension with nine items was named “Awareness” and “Benefits” for the second dimension with seven items. Factor loadings of the items changed from 0.433 to 0.838 and communalities changed from 0.312 to 0.649 (Table 2). Items, factor loadings, and communalities of the 16 items were presented in Table 2. Additionally, the results in Table 3 showed that there was a positive and significant correlation between the subscale’s factor scores (r = 0.674, p < 0.001).

4.3. Confirmatory Factor Analysis of the AoDiG-Sign Scale

Confirmatory factor analysis (CFA) was performed to verify the factor structure obtained in EFA on the second sample (n2 = 278). Based on the fit indices obtained as a result of CFA, it was evaluated whether the factors have a valid structure. The maximum likelihood (ML) method was used to provide parameter estimates of the CFA model. In the CFA, it was tested if the dataset provides a good fit to the two-factor model obtained in EFA. Several statistics were calculated to determine how well the model fits the data. The main idea of comparison fit indices is that the fit of a target model is compared to the fit of an independence or baseline model. In this study, Chi-square  ( χ 2 ) , Chi-square/degrees ( χ 2 / d f ), Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), Normed Fit Index (NFI), Nonnormed Fit Index (NNFI) or Tucker-Lewis Index (TLI), Comparative Fit Index (CFI), Incremental Fit Index (IFI) were taken into account as model fit indices.
When the goodness of fit indices of the two-factor model presented in Table 4 were examined, the intervals between a good fit and an acceptable fit were indicated [40]. The model fit was interpreted according to the conventional threshold values of the fit indices. According to the results presented in Table 4, it has been seen those values of  χ 2  and  χ 2 / d f  were acceptable as 35.635 and 2.545, respectively. In addition, the fit of the model was good according to the value of SRMR = 0.0471; for the RMSEA = 0.0747, it can be said that the model is acceptable. When the NFI and IFI values are examined, the fact that both index values are greater than 0.95 shows that they have a good fit (NFI = 0.9721, IFI = 0.9722). On the other hand, the CFI value greater than 0.97, indicating that the two-factor model fits well (CFI = 0.9828) and the TLI value of 0.9581 shows that it has an acceptable fit since for TLI, between 0.95 and 0.97 indicates acceptable fit. Lastly, according to rule-of-thumb criteria, the fit of the model for the AGFI index was good; for the GFI value, it can be said that the model is acceptable (AGFI = 0.9152, GFI = 0.9412). When the fit indices’ values are examined, we can conclude that all fit indices values verify the suggested two-factor model form of the AoDiG-sign and the two-factor model indicates a good fit and is applicable.

4.4. Convergent Validity and Discriminant Validity

To ensure convergent validity, the Composite Reliability (CR) and Cronbach’s Alpha (CA) values for each construct should be greater than 0.70 [41]. Additionally, the Average Variance Extracted (AVE) value of each factor should be higher than 0.50 [42]. In addition, it was stated that in cases where the CR values of the relevant factor are greater than 0.70, it is acceptable for the AVE to be less than 0.50 and the convergent validity is sufficient [41,42]. For our scale, the AVE value of the “Awareness” was greater than 0.5 (AVE = 0.506) and the CA = 0.888 and CR = 0.899 values were greater than 0.7. Although the AVE value of the “Benefits” was very close to 0.5 (AVE = 0.467), it was less than 0.5, but the CA = 0.790 and CR = 0.856 values were greater than 0.7, as seen in Table 5. For this reason, we can say that the AoDiG-sign scale has provided convergent validity.
Discriminant validity was examined using the Fornell-Lacker criterion [42] and Heterotrait-monotrait (HTMT) [43] criterion techniques. According to the Fornell-Larcker criterion, the discriminant validity of the scale is checked by comparing the square root of the Average Variance Extracted (AVE) value of each factor with the correlation between the factors. As a result of these comparisons, discriminant validity is ensured if the square root value of AVE for each factor is higher than the correlation coefficient between the factors [42]. Heterotrait-monotrait (HTMT) ratio of correlation is the other criterion of discriminant validity [43]. When HTMT values are very close to 1, it is concluded that there is no discriminant validity. If the HTMT value is greater than the cutoff value of 0.85, it can be concluded that there is no discriminant validity. When the discriminant validity of the developed scale was examined according to the Fornell-Larcker criterion, as seen in Table 5, the square root values of AVE for the factors (Awareness = 0.711, Benefits = 0.683) were higher than the correlation coefficient between the factors (r = 0.674). This result shows that the discriminant validity of the scale has been provided according to the Fornell-Larcker criterion. Additionally, when we examined the discriminant validity according to the HTMT criterion, we observed that the HTMT value (0.838) was less than 0.85, and thus, it was determined that the discriminant validity was also satisfied according to the HTMT criterion.

4.5. Reliability of the AoDiG-Sign Scale

The AoDiG-sign scale demonstrated adequate internal consistency since Cronbach’s alpha value of all items was 0.889 [44]. Both AoDiG-sign dimensions showed well internal consistency since Cronbach’s alpha value of the first subfactor, “Awareness” with 9 items was 0.888 and the Cronbach’s alpha value of the second subfactor, “Benefits” with 7 items was 0.790. Furthermore, no substantial increment in Cronbach’s alpha value was observed when eliminating any item for both subfactors. The Spearman-Brown Split Half Reliability Coefficient of the 16-item AoDiG-sign scale was also good (r = 0.771). Both AoDiG-sign subscales had also good values with Spearman-Brown Split Half Reliability Coefficient of 0.889 (Awareness) to 0.785 (Benefits). The test-retest reliability was investigated by Pearson’s r and ICC from the AoDiG-sign scale scores of 100 persons after two weeks of the initial questionnaire application. According to ICC and Pearson’s r, it was observed that the AoDiG-sign scale scores were consistent over the two-week period (ICC = 0.986, p < 0.001 and Pearson’s r = 0.976, p < 0.001).

4.6. Item Analysis of the AoDiG-Sign Scale

Item analysis results are presented in Table 6. It can be seen that the item-total correlation changed from 0.353 to 0.680 for the entire items in the scale. The item-total correlation coefficients of the items were greater than the cutoff value, 0.300. Moreover, the item-total correlation of all items was greater than 0.300 for each subfactor. In addition, if the items in the scale were eliminated individually, Cronbach’s Alpha value was not higher than Cronbach’s Alpha value of the overall items (0.889). Nevertheless, no increase in alpha value was observed when any item was removed for each subfactor. These item analysis findings showed that it was appropriate for all 16 items to remain on the AoDiG-sign scale. Additionally, when applying Student’s t-test for the comparison of the average scores of the upper 27% (n = 75) and lower 27% (n = 75) groups to all 16 items, it is observed that there was a significant difference between these two groups since t-test values varied between 7.078 (p < 0.001) and 16.227 (p < 0.001). These results can be evaluated as the items in the AoDiG-sign scale have superior validity and are the items that measure the same feature. It can be said that all items of the scale distinguish between those who have the feature and those who do not (in other words, it can reveal differences between participants).

5. Discussion

Technological developments affect all sectors, especially businesses. These new technological trends force businesses to digitize all their operations using digital documents, and by the usage of digital signatures. A handwritten signature is in the form of a written stamp of the signatory placed in a document to assure that the signatory has read and is happy with the contents of the document. A digital signature is the digital version of a written signature placed in a document by the signatory [45], where the document can be displayed and read on digital devices such as mobile phones, laptops, tablets, etc. Nowadays, handwritten signatures are outdated [11,46] and digital signatures have become very important alternative tools used to implement security as well as correct signatures.
Handwritten signatures (also called conventional signatures) require a document to be available in paper form and then they are signed in ink by the signatory. Conventional signatures have some important features that make them attractive [7]: It is relatively easy to verify their authenticity, forging and altering a handwritten signature is rather difficult, and also the signatory cannot later deny signing the document. Perhaps another attractive feature of handwritten signatures is that there is no need to use a digital device and anyone of any age and ability can sign a paper document without the need to know how to use a digital device. A digital signature needs to have at least all the above-mentioned features of a conventional signature, and ideally, it should include additional security features for sensitive applications such as wills, trusts, inheritance issues, conveyancing, bank transactions, and similar secure transactions carried over the Internet. As the speed of business communications increases, so does the need for fully automated offices. Moreover, as a result of the COVID-19 pandemic, it has not been possible for most people to meet and sign handwritten documents. For this reason, documents were signed securely using encrypted digital signatures over web-based cloud storage services supporting digital signatures [20]. However, as with important paper documents, digital documents needed to have a marker to guarantee their authenticity. The solution has been to attach digital signatures to digital documents to maintain their authenticity [47]. Additionally, the authenticity of the authors’ digital signatures was put on the documents and maintained with the help of cryptographic methods [48].
The concept of a paperless office is growing rapidly as paper-based documents are being replaced with electronic ones [49]. As a result of this development, the need and popularity of digital signatures have been growing rapidly in society. Authenticity, trust, and traceability have always been important issues in verifying a signatory. With the recent advances in digital technology, digital signatures nowadays provide more authenticity when compared with conventional handwritten signatures [45]. The signing process in a digital signature is implemented using public-key cryptography [14], where the signatory uses a private key to create the digital signature. Therefore, it is not possible to forge a digital signature since the public key will not match the private key used during the signing. If the signature or the document content is changed after the document is signed, the hash values will be different and this will automatically indicate forgery. The encryption and decryption algorithms used in the digital signature process enhance security as well as reduce the overall system complexity [50]. A digital signature includes an automatically generated date and time stamp, which is necessary for legal documents in most fields of business for sustainability.
The scale developed in this study aims to determine individuals’ awareness of digital signatures. The analysis made according to statistical methods showed that the developed scale was both valid and reliable. The comprehensive validity analysis based on content validity, construct validity, convergent validity, and discriminant validity indicated that the developed scale titled AoDiG-sign is valid. In addition, the detailed reliability analysis based on test-retest, and internal consistency demonstrated that the AoDiG-sign scale is reliable. As a result, the scale developed in this study can be used in scientific studies to determine people’s digital signature awareness. We hope that this will make a significant contribution to the initiation and/or positive realization of digital transformation processes by various institutions and organizations, especially in developing countries.
Features such as the convenience of using a digital signature (many documents can be signed at the same time with a single click, etc.), security, fast transmission, and approval have led to an increase in the popularity of the digital signature by businesses, especially during the COVID-10 pandemic period [13]. For this reason, determining the digital signature awareness of people in society will enable the necessary measures to be taken. In this context, it is thought that the scale developed within the scope of the study will make a significant contribution to the literature and fill the existing gap in the literature.

6. Conclusions and Future Scope

The result of digitization has led to the transfer of manual/paper-based correspondence to digital platforms. The closure of all sectors such as workplaces and schools, especially during the COVID-19 pandemic period, has caused employers and employees, teachers and students to work online from their homes. Whether there is a pandemic or not, the continuation of the existence of the sectors and their participation in the economy depend on the continuation of their transactions under all possible conditions. At this point, transactions and approvals can be made in digital platforms with the opportunities provided by digital signatures, and the workflow can continue without interruption. For this reason, users should first be provided with digital signature awareness. However, in the literature, the digital signature awareness scale has not yet been developed. For this reason, people’s digital signature awareness cannot be determined. This is an important gap in the process of digitization and digital transformation of all types of businesses. If people do not have digital signature awareness or do not know the benefits of digital signature usage, the digital transformation process will fail. Because it can be very difficult for them to give up their habits, namely using paper-based signatures, which have survived until today, and in some cases, they may even show strong resistance to using digital signatures. For this reason, this descriptive research was carried out to develop a digital signatures awareness scale named AoDiG-sign. At the end of the study, the comprehensive results found that the developed scale validate and reliable for scientific research and the developed digital signature awareness scale can be used in scientific studies. Thus, the scale, which was felt to be lacking in this subject in the literature, has been acquired. We hope that the developed scale will be used by shareholders of all types of institutions and organizations to determine their staff’s digital signature awareness. As a result of the digital signature awareness determination, all types of organizations should start digitalization in all their transactions, or if they have already started, they should reach success in digital transformation processes.
In the future, with the developed scale, it is planned to carry out studies to determine the digital signature awareness of individuals in developing countries, especially in Cyprus for sustainable businesses. Thus, by organizing the training needed according to the awareness levels of the citizens, it will be possible for the sectors in developing countries to compete with other countries by using digital signature systems and consequently communicate, cooperate, and expand their businesses. For developing countries like Cyprus which desires to open up to the world, it is of vital importance to be able to compete with the rest of the world in order to exist. Moreover, it is recommended for researchers to collect data with this developed scale and compare the results from organizations that have completed digitalization and digital transformation processes and organizations that have not completed these processes or are at the beginning of the processes. As with all scientific studies, this study is limited to the items contained in the developed scale.

Author Contributions

Conceptualization, N.C. and N.S.; methodology, N.C. and N.S.; investigation, N.C. and N.S.; data curation, N.S.; writing—original draft preparation, N.C. and N.S.; writing—review and editing, N.C. and N.S.; visualization, N.C. and N.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was approved by the Scientific Research Ethics Committee of Near, East University with an approval number NEU/AS/2021/141 dated 8 December 2021.

Informed Consent Statement

Informed consent was obtained from all the participants involved in this study.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author.

Acknowledgments

The authors would like to thank all those people who participated in this survey and filled out the questionnaire, and the authors offer a special thanks to Hamza Wahab for his support during the data collection process. In addition, the authors would like to point out that this study was presented as an extended abstract at the International Conference on Analysis and Applied Mathematics (ICAAM 2020) and also published in AIP Conference Proceedings.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 05008 g001 550
Figure 1. Scree Plot.
Figure 1. Scree Plot.
Sustainability 15 05008 g001
Table
Table 1. Content Validity measures.
Table 1. Content Validity measures.
ItemCVRI-CVI
(Relevance)		I-CVI
(Simplicity)		I-CVI
(Clarity)
1. Digital signature is one of the secure methods for online transactions such as online payments, online banking, etc.0.810.80.9
2. Digital signature gives me instant response.0.810.91
3. Date can be used to confirm the validity by digital signature.−0.80.10.10.1
4. Digital signature speeds up the process of collecting signatures from authorized staff/personnel.1111
5. The ability to use digital signature provides advantages to employers in businesses.−0.20.30.20.3
6. The confirmation process is speeded up using digital signature.110.90.9
7. Signing documents using digital signature cannot be denied afterwards.0.8110.8
8. Digital signature is a new trend in digital transformation process in a business.0.8111
9. Digital signature is compulsory in e-commerce processes.1111
10. Special software is required to use digital signature.−0.80.10.10.1
11. Digital signature reduces operational costs.1110.9
12. Documents can be authenticated in one click using digital signature.0.8111
13. Digital signature is environmentally friendly as it does not require paper.0.80.910.9
14. Security is enhanced using digital signature.0.80.910.9
15. Digital Signature improves all stakeholders’ satisfaction.−10.10.10
16. Digital signature is a digital process used online.0.8110.9
17. Digital signature is used for e-governance.0.8111
18. Aged people will have difficulties adapting to digital signature because it involves new technology.0.810.80.9
19. With the help of digital signatures, I can connect to government sites, hospitals, and banks.1111
20. Documents can easily be archived after digital signature.0.810.91
21. The use of digital signature reduces the cost of transferring documents among stakeholders.−0.20.30.10.2
22. It is easy to prove who owns the documents signed with a digital signature.0.8110.9
23. Documents are authenticated in less time using digital signature.0.8111
24. Digital signature reduces the workload as it removes the need to sign many papers at once.110.91
25. Digital signatures facilitate following the documents whether they are signed or not. 0.810.90.9
Table
Table 2. Items, factor loadings, and communalities for the awareness of the users towards digital signature.
Table 2. Items, factor loadings, and communalities for the awareness of the users towards digital signature.
Item No StatementsFactor LoadingCommunality
Factor 1: Awareness
1.Documents can be authenticated in one click using digital signature.0.8380.649
2.Digital signature is used for e-governance.0.8180.573
3. Digital signature is compulsory in e-commerce processes.0.7930.542
4. With the help of digital signature, I can connect to government sites, hospitals, and banks.0.7570.557
5. Digital signature gives me instant response.0.7460.557
6. Digital signature is one of the secure methods for online transactions such as online payments, online banking, etc.0.7180.607
7. Digital signature is a digital process used online. 0.6200.576
8. Documents can easily be archived after digital signature. 0.4980.388
9. Digital signature is environmentally friendly as it does not require paper.0.4480.425
Factor 2: Benefits
10. Digital signature speeds up the process of collecting signatures from authorized staff/personnel.0.7500.533
11. The security is enhanced using digital signature.0.7470.525
12. Documents are authenticated in less time using digital signature.0.7010.764
13. The confirmation process is speeded up using digital signature.0.6490.649
14. Digital signature reduces the workload as it removes the need to sign many papers at once.0.6460.390
15. Digital signature reduces operational costs.0.5720.312
16. Signing documents using digital signature cannot be denied afterwards.0.4330.387
Extraction Method: Principal Component Analysis, Rotation Method: Oblimin with Kaiser Normalization.
Table
Table 3. Mean  x ¯ , standard deviation (s), and correlation (r) between two subfactors of AoDiG-sign scale.
Table 3. Mean  x ¯ , standard deviation (s), and correlation (r) between two subfactors of AoDiG-sign scale.
AoDiG-SignAoDiG-Sign x ¯ Sr
Awareness93.74
3.70		0.670.674 **
Benefits70.77
** p < 0.001.
Table
Table 4. Fit indices values of Confirmatory Factor Analysis of the AoDiG-sign scale.
Table 4. Fit indices values of Confirmatory Factor Analysis of the AoDiG-sign scale.
Fit IndexValueRecommended Threshold Value
Good FitAcceptable Fit
  χ 2 35.635   0 ≤ χ 2 ≤ 2 d f 2df <  χ 2 ≤ 3 d f  (df = 14)
  χ 2 / d f 2.5454   0 ≤ χ 2 / d f ≤ 2   2 < χ 2 / d f ≤ 3
RMSEA 0.0747   0 ≤ R M S E A ≤ 0.05   0.05 < R M S E A ≤ 0.08
SRMR0.0471   0 ≤ S R M R ≤ 0.05   0.05 < R M S E A ≤ 0.10
NFI0.97210.95  ≤ N F I ≤ 1.00 0.90  ≤ N F I < 0.95
TLI (NNFI) 0.95810.97  ≤ T L I ≤ 1.00 0.95  ≤ T L I < 0.97
IFI0.97220.95  ≤ I F I ≤ 1.00 0.90  ≤ I F I < 0.95
CFI 0.98280.97  ≤ C F I ≤ 1.00 0.95  ≤ C F I < 0.97
GFI0.94120.95  ≤ G F I ≤ 1.00 0.90  ≤ G F I < 0.95
AGFI0.91520.90  ≤ A G F I ≤ 1.00 0.85  ≤ A G F I < 0.90
Table
Table 5. CA, CR, AVE, square root of AVE values (on diagonal in bold) for Fornell-Lacker criterion, correlation coefficient between factors (off-diagonal), and HTMT value.
Table 5. CA, CR, AVE, square root of AVE values (on diagonal in bold) for Fornell-Lacker criterion, correlation coefficient between factors (off-diagonal), and HTMT value.
FactorsCACRAVEFornell-LarckerHTMT
Awareness BenefitsAwareness Benefits
Awareness0.8880.8990.5060.711--
Benefits0.7900.8560.4670.6740.6830.838-
Table
Table 6. Item analysis results.
Table 6. Item analysis results.
Items   x ¯   s Median (IQR)Corrected İtem-Total CorrelationCronbach’s Alpha if İtem Deletedt
(Lower 27%-Upper 27%)
Factor 1: Awareness
1.3.92 (1.05)4 (1)0.6320.8799.265 ***
2.3.67 (1.05)4 (1)0.3530.8897.328 ***
3.3.56 (1.18)4 (1)0.5450.88312.665 ***
4.3.61 (1.10)4 (1)0.5020.88411.265 ***
5.3.92 (1.16)4 (1)0.6120.88011.466 ***
6.3.83 (1.15)4 (2)0.5880.88114.037 ***
7.3.74 (1.14)4 (2)0.4570.8867.578 ***
8.3.73 (1.09)4 (2)0.6800.87710.139 ***
9.3.64 (1.13)4 (1)0.6700.87711.951 ***
Factor 2: Benefits
103.69 (1.01)4 (1)0.5200.8838.962 ***
113.65 (1.11)4 (2)0.3840.88810.905 ***
123.85 (1.12)4 (2)0.5800.88111.319 ***
133.69 (1.07)4 (1)0.4740.8858.797 ***
143.74 (1.13)4 (2)0.6000.88016.227 ***
153.61 (1.01)4 (1)0.5380.8837.078 ***
163.71 (0.99)4 (1)0.5460.88210.502 ***
*** p < 0.001.
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Cavus, N.; Sancar, N. The Importance of Digital Signature in Sustainable Businesses: A Scale Development Study. Sustainability 2023, 15, 5008. https://doi.org/10.3390/su15065008

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Cavus N, Sancar N. The Importance of Digital Signature in Sustainable Businesses: A Scale Development Study. Sustainability. 2023; 15(6):5008. https://doi.org/10.3390/su15065008

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Cavus, Nadire, and Nuriye Sancar. 2023. "The Importance of Digital Signature in Sustainable Businesses: A Scale Development Study" Sustainability 15, no. 6: 5008. https://doi.org/10.3390/su15065008

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