Abstract
Integrating social client relationship management (CRM 2.0) in the built environment can enhance the relationship between construction organizations and client towards sustaining a long and lasting collaboration. The data exploration analyzed the e-readiness of contracting and consulting construction firms in the uptake of CRM 2.0 and the barriers encountered in the adoption of the modern business tool. The targeted organizations consist of seventy five (75) construction businesses operating in Lagos State which were selected from a pool of registered contracting and consulting construction firms using random sampling technique. Descriptive statistics of the e-readiness of contracting and consulting construction firms for CRM 2.0 adoption and barriers limiting its uptake were analyzed. Also, inferential analysis using Mann–Whitney U statistical and independent sample t-test was performed on the dataset obtained. The data generated will support construction firms on the necessity to engage in client social relationship management in ensuring sustainable client relationship management in the built environment.
Keywords: Client, Contractors, Construction industry, Relationship management, Social media
Specifications table
| Subject area | Construction Management. |
|---|---|
| More specific subject area | Relationship Management. |
| Type of data | Tables and Figures |
| How data was acquired | Cross-sectional Survey design |
| Data format | Raw, analyzed. |
| Experimental factors | Random sampling of Construction organizations |
| Experimental features | Readiness and Barriers to Client Social Relationship Management (CRM 2.0) adoption by construction organizations |
| Data source location | Lagos, Nigeria. |
| Data accessibility | All the data are in this data article |
Value of the data
-
•
Clients are the most important entity in the construction business, therefore managing their needs is paramount to the success of construction organizations.
-
•
The dataset will enable researchers to advance on the subject of social client relationship management in the built environment as there is a dearth of studies in this area as it relate to the construction industry [1], [2], [3], [4].
-
•
An understanding of the data when analyzed compared with existing data on client relationship management can help ascertain the sustainable route to managing client relationship in the built environment.
-
•
The dataset can help construction stakeholders understand the barriers militating against the uptake of client social relationship management so as to develop a framework that can successfully increase the uptake of ICT tools and other relationship management tools in the built environment.
-
•
Clients are the most important entity in the construction business, therefore managing their needs is paramount to the success of construction organizations.
1. Data
Social client relationship management (CRM 2.0) is a business strategy that uses internet and social media platforms to enhance traditional, one-dimensional interactions between companies and their existing and potential customers by giving clients greater control over how they communicate with the firms they do business with and providing them with the tools needed to form the foundation of the relationship. In order to measure the relationship management that exist between clients and construction organization, the data exploration covered contractor and consultant construction firm's readiness to uptake social client relationship management (CRM 2.0) in the built environment and barriers militating against its uptake. The necessity of the data on Client Relationship Management uptake is due to the slow rate of adoption of the modern client management tool in the built environment compared to other industries like telecommunication and logistic enterprise [1], [2], [3], [4]. A questionnaire instrument was retrieved from seventy five (75) construction organizations which included contracting and consulting firms. The data focused on the presence and use of different social platforms by construction firms as shown in Table 1. In Table 1, the overall mean score showed that social media presence is mostly maintained by contracting and consulting firms via personal websites owned by the firm, Facebook and LinkedIn. This means that efforts by the construction business to reach out to clients in order to manage their relationships are attained using these social media platforms. The data generated is necessary due to the relevance of the social platforms in the modern client relationship management. Likewise, data are garnered on the barriers militating against the uptake of the social client relationship management (CRM 2.0) which is presented in Table 2. In Table 2, the overall mean score revealed that the most significant barriers encountered by construction businesses in the use of social client relationship management (CRM 2.0) include lack of business strategy by organizations, Lack of control over social media use and size of the construction organizations. The dataset revealed that construction business must consciously and judiciously draft a business strategy that includes relationship management in order to survive the construction terrain. When the data is analyzed, further inferential statistical decisions can be made from the data exploration. Inferential statistics such as Mann–Whitney U test which measured if there was any significant difference between contracting and consulting firms on the use of social media platforms for social client relationship management was tested (Table 3, Table 4, Table 5). In Table 4, the p-value at .601 showed that there was no significant difference between contracting and consulting firms on the use of social media platforms for social client relationship management. This means that both contracting and consulting firms aligned in the social media platforms used for social client relationship management in their construction business. Furthermore, independent sample t-test was conducted to ascertain if there was significant difference between contracting and consulting firms on barriers militating against the adoption of social media in social client relationship management (CRM 2.0) as presented in Table 6, Table 7. In Table 7, the p-value at .40 and .36 which is higher that the alpha value of .05 depicts that there was no significant difference between contracting and consulting firms on barriers militating against the adoption of social media in social client relationship management (CRM 2.0) in their construction business.
Table 1.
Social media presence of construction organizations.
| Social media platforms | Contracting firms | Consulting firms | Overall mean score |
|---|---|---|---|
| Mean score | Mean score | ||
| Company's own website | 3.41 | 3.31 | 3.43 |
| 3.00 | 3.62 | 3.28 | |
| 2.87 | 3.34 | 3.00 | |
| Google+ | 2.70 | 2.90 | 2.73 |
| WordPress | 2.50 | 2.48 | 2.48 |
| 2.22 | 2.07 | 2.16 | |
| 1.93 | 1.62 | 1.86 | |
| Social bookmarking sites | 1.67 | 1.55 | 1.63 |
| YouTube | 1.57 | 1.62 | 1.59 |
| Blogger | 1.35 | 1.24 | 1.32 |
| Snapchat | 1.28 | 1.38 | 1.32 |
| 1.26 | 1.62 | 1.38 | |
| Flickr | 1.24 | 1.24 | 1.24 |
| Yammar | 1.17 | 1.21 | 1.18 |
| Vimeo | 1.13 | 1.28 | 1.18 |
Table 2.
Barriers to organizations readiness for CRM 2.0 adoption.
| Barriers | Contracting firm | Consulting firm | Overall mean score |
|---|---|---|---|
| Mean Score | Mean Score | ||
| Lack of business strategy by organizations | 2.65 | 3.10 | 2.82 |
| Lack of control over social media use | 2.70 | 2.96 | 2.81 |
| Construction organizations size | 2.54 | 3.03 | 2.77 |
| Managements unwillingness to adopt new technology | 2.54 | 3.03 | 2.77 |
| Complex nature of client-organization relationship | 2.59 | 2.96 | 2.76 |
| Organizations lack of investment on social software management tools | 2.70 | 2.75 | 2.72 |
| Organizations lack of knowledge of social media client management capacity in the built environment. | 2.60 | 2.79 | 2.71 |
| Managements negative perception about social platforms. | 2.35 | 2.82 | 2.54 |
| External pressure from competitors | 2.45 | 2.42 | 2.49 |
| Fear of clients information leakages by social platforms managers | 2.36 | 2.46 | 2.41 |
Table 3.
Mean ranks of differences in contracting and consulting organizations use of social media for CRM 2.0.
| Organization type | N | Mean rank | Sum of ranks | |
|---|---|---|---|---|
| Social Media Platform | contracting | 46 | 36.96 | 1700.00 |
| consulting | 29 | 39.66 | 1150.00 | |
| Total | 75 |
Table 4.
Mann–Whitney U test statistics of differences in contracting and consulting organizations use of Social media for CRM 2.0.
| Social media platform | |
|---|---|
| Mann–Whitney U | 619.000 |
| Wilcoxon W | 1700.000 |
| Z | −.523 |
| Asymp. Sig. (2-tailed) | .601 |
Table 5.
Median score of differences in contracting and consulting organizations use of social media for CRM 2.0.
| Organization type | N | Median |
|---|---|---|
| Contracting | 46 | 26.0000 |
| Consulting | 29 | 30.0000 |
| Total | 75 | 27.0000 |
Table 6.
Mean rank of difference in contracting and consulting construction organizations on the barriers to CRM 2.0 adoption.
| Organization type | N | Mean | Std. deviation | Std. error mean | |
|---|---|---|---|---|---|
| Challenges | Contracting | 46 | 25.5000 | 5.90198 | .87020 |
| Consulting | 28 | 28.3571 | 5.35561 | 1.01211 |
Table 7.
Independent sample t-test on difference in contracting and consulting construction organizations' perception on barriers to social media adoption in CRM 2.0.
| Barriers |
|||||
|---|---|---|---|---|---|
| Equal variances assumed |
Equal variances not assumed |
||||
| (Upper) | (Lower) | (Upper) | (Lower) | ||
| Levene's Test for equality of variances | F | .225 | |||
| Sig | .637 | ||||
| t-Test for equality of means | T | −2.090 | −2.141 | ||
| Df | 72 | 61.506 | |||
| Sig. (2 tailed) | .40 | .36 | |||
| Mean difference | −2.857 | −2.857 | |||
| Standard error difference | 1.367 | 1.334 | |||
| 95% Confidence interval of the difference | −.132 | −5.582 | −.188 | −5.526 | |
2. Experimental design, materials and methods
The population for the data exploration is the summation of construction organizations which comprises of consulting and contracting firms in Nigeria. The dataset collected is more specific to construction business of contracting and consulting firms operating in Lagos State. Lagos State has many head offices of construction organizations within the state. The contracting and consulting construction firms used to generate the data were selected randomly from the pool of the record of registered construction organizations in the study area. A cross-sectional research survey design was used in selecting the sample size of seventy-five (75) construction organizations. Questionnaire instruments were directed to managerial staff that deal with client and client organizations. Similar field surveys that have obtained dataset in like manner include [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18]. In future studies, the client or client organizations' perspective can be measured as regards the effectiveness of contracting and consulting firms to the issues raised on construction projects. The dataset can be replicated in other climes and compared with analysis in this data exploration.
Acknowledgements
The researchers acknowledge the publication support by Covenant University through the Covenant University Centre for Research, Innovation and Development (CUCRID), Ota, Ogun State.
Footnotes
Supplementary data associated with this article can be found in the online version at 10.1016/j.dib.2018.04.037.
Transparency document. Supplementary material
Supplementary material
.
References
- 1.Swarts K.M., Lehman K., Lewis G.K. The use of social customer relationship management by building contractors: evidence from Tasmania. Constr. Manag. Econ. 2016;34(4–5):302–316. [Google Scholar]
- 2.Bresnen M., Marshall N. Building partnerships: case studies of client contractor collaboration in the UK construction industry. Constr. Manag. Econ. 2000;18(7):819–832. [Google Scholar]
- 3.Preece C., Chong H.Y., Golizadeh H., Rogers J. A review of customer relationship (CRM) implications: benefits and challenges in construction organizations. Int. J. Civ. Eng. 2014;13(3):362–371. [Google Scholar]
- 4.Li Y., Xiao Z., Liu F.S. Classification of clients in Client Relationship Management (CRM) base on rough set theory. Int. Conf. Mach. Learn. Cybern. 2003;1:242–246. [Google Scholar]
- 5.Ojelabi R.A., Oyeyipo O.O., Afolabi A.O. Built environment professional perceptions of the effectiveness of building control measures in Lagos State. J. Constr. Dev. Countries. 2017;22(1):41–54. [Google Scholar]
- 6.Afolabi A., Emeghe I., Oyeyipo O., Ojelabi R. Professionals' preference for migrant craftsmen in Lagos State. Med. J. Soc. Sci. 2016;7(1):501–508. [Google Scholar]
- 7.Oyeyipo O.O., Odusami K.T., Ojelabi R.A., Afolabi A.O. Factors affecting contractors' bidding decision for construction projects in Nigeria. J. Constr. Dev. Countries. 2016;21(2):21–35. [Google Scholar]
- 8.Ogunde A.O., Olaolu O., Afolabi A., Owolabi, J J., Ojelabi R. Challenges confronting construction project management system for sustainable construction in developing countries: professionals perspectives (a case study of Nigeria) J. Build. Perform. 2017;8(1):1–11. [Google Scholar]
- 9.L. Amusan, P. Tunji-Olayeni, A. Afolabi, I. Omuh, R. Ojelabi, A. Oluwatobi, Remodularising technical institutions towards quality manpower delivery in construction sector in Nigeria, in: Proceedings of the 10th Annual International Technology, Education and Development Conference, 7th–9th March, Valencia, Spain, 2016.
- 10.Tunji-Olayeni P., Emetere M.E., Afolabi A. Multilayer perceptron network model for construction material procurement in fast developing cities. Int. J. Civ. Eng. Technol. 2017;8(5):1468–1475. [Google Scholar]
- 11.A. Afolabi, M. Dada, An evaluation of the factors affecting housing and urban development projects in Lagos State. Paper presented at the Proceeding of CIB W107 International Conference on Construction in Developing Countries and its Contribution to Sustainable Development, University of Lagos, Lagos, Nigeria, 28th–30th January, 2014.
- 12.Bishop S.A., Owoloko E.A., Okagbue H.I., Oguntunde P.E., Odetunmibi O.A., Opanuga A.A. Survey Datasets on the externalizing behaviors of primary school pupils and secondary school students in some selected schools in Ogun State, Nigeria. Data Brief. 2017;13:469–479. doi: 10.1016/j.dib.2017.06.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Dimara E., Manganari E., Skuras D. Survey data on factors influencing participation in towel reuse programs. Data Brief. 2017;10:26–29. doi: 10.1016/j.dib.2016.11.068. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Canesi R., Marella G. Residential construction cost: an Italian survey. Data Brief. 2017;11:231–235. doi: 10.1016/j.dib.2017.02.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Okagbue H.I., Opanuga A.A., Oguntunde P.E., Ugwoke P.O. Random number datasets generated from statistical analysis of randomly sampled GSM recharge cards. Data Brief. 2017;10:269–276. doi: 10.1016/j.dib.2016.12.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Popoola S.I., Atayero A.A., Badejo J.A., John T.M., Odukoya J.A., Omole D.O. Learning analytics for smart campus: data on academic performances of engineering undergraduates in Nigerian private university. Data Brief. 2018;17:76–94. doi: 10.1016/j.dib.2017.12.059. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Popoola S.I., Atayero A.A., Okanlawon T.T., Omopariola B.I., Takpor O.A. Smart campus: data on energy consumption in an ICT-driven university. Data Brief. 2018;16:780–793. doi: 10.1016/j.dib.2017.11.091. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Afolabi A.O., Ojelabi R.A., Tunji-Olayeni P.F., Fagbenle O.I., Mosaku T.O. Survey datasets on women participation in green jobs in the construction industry. Data Brief. 2018;17:856–862. doi: 10.1016/j.dib.2018.02.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary material