The impact of work location models and adaptive performance in oil and gas firms in Rivers State
Category : Latest Publications
THE IMPACT OF WORK LOCATION MODELS AND ADAPTIVE PERFORMANCE IN OIL AND GAS FIRMS IN RIVERS STATE.
1Josiah-Hart Inyingiwarifagha Valerie & 2Prof. Omoankhanlen Joseph Akhigbe
1,2Department of Management, University of Port Harcourt
Abstract
This study empirically examines the extent to which work location models relates with adaptive performance in oil and gas industry. Hybrid and remote work location models were considered the dimensions of work location models against adaptive performance. The accessible population of this study was 656 administrative staff from five (5) selected oil and gas firms in Rivers State. The study population was chosen due to easy accessibility to information and for an unbiased conclusion in order to achieve the aim of the study. The sample size for this study was 248 which was obtained using the Taro Yamane’s formula. The primary data was collected with the aid of structured questionnaires which were administered to respondents via online survey. Spearman Rank Order Correlation Co-efficient statistical analysis was employed in analysing the hypotheses in order to determine the correlation between the variables with the aid of Statistical Package for Social Sciences (SPSS). Our findings revealed a significant correlation between on site work and task performance. The study recommended that management of oil and gas firms should embark on proper planning and execution to incorporate the several work location models so as to encourage flexibility and adaptability in order to survive the environmental changes associated with the business world.
Keywords: Adaptive performance, oil and gas, work location, performance, Hybrid model, remote model
Introduction
Work location Models are work arrangements formulated by the management of an organization and its top executives as regards the definition of objectives, motivation, coordination of activities and allocation of resources (Birkinshaw& Goddard, 2009). Georgetown University Law Center termed WLM Flexible Work Arrangements (FWA) and defined it as a work structure that alters the time and/or place that work gets done regularly (Sloan, 2010). Work location models simply put, are work arrangements provided or formulated by management as a form of motivation to leave the employee open to choices of preferred work location. Work location models help managers organize the resources and activities of the organization towards achieving organizational goals. Work location models identify with the fact that there is no “one- size-fits-all” approach to work; rather each firm develops a model based on the needs of the firm and the preference of the employee (Griffis, 2021).
Before COVID, all jobs were done onsite except in rare cases where the organization permits work away from the office, also known as telework. Since 2020, traditional work locations have been altered by the pandemic, it drastically increased the rate of change of work models thereby providing employees richness of choice, flexibility and autonomy- humanizing work (Sharply, Simpson, Miller & Dunne, 2020). However, the concept of alternative work location models is not new to the world of business as the idea of telework was first developed in the 1970s by Nilles to describe an alternative work arrangement aided by information communication technology (ICT) as a means of avoiding daily commuting (Nilles, 1975; Nilles, 1976 & Pratt, 1984). After which a number of researchers have experimented on the concept, one of which was in 1994 in a telephone company in the United States of America, AT&T where 32,000 employees worked from home including the CEO. The purpose of this experiment was to examine how much a vast organization could thrive if the workplace is transformed and work is moved to employee, not the employee moving to work (Apgar, 1998). Alternative work location models were examined by managers for a number of reasons; to reduce cost as seen in AT&T’s ability to save up to $550 million in cash-flow from 1991 to 1998 by eliminating offices, to increase productivity, to make the organization more appealing to talented and highly motivated employees, etc (Apgar, 1998). In today’s reality, due to COVID-19 pandemic, the traditional workplace is being challenged with several companies evolving to alternative work models for business continuity. An analysis conducted by Delloite, show that 31% of the workforce in Australia (approximately 4 million) and 50 million across the six nations of ASEAN could shift permanently to alternative work arrangements (Delloite, 2020). Fluegge (2009) in his study of the impact of recreation on job performance divided employee job performance into task performance, organizational citizenship behavior and creative performance; creative performance being a behavioural manifestation of creativity, i.e. the ability to generate ideas, procedures and products both original and useful.
Allworth and Hesketh (1999); Pulakos, Arad. Donovan and Plamondon (2000) and Griffin et al. (2007) focused on the growing interdependency and uncertainty of work systems and the corresponding change in the nature of employee job performance. All three argued that adaptive performance should be a separate dimension of employee job performance. Adaptive performance is defined as the extent to which an individual adapts to changes in a work system or work roles (Griffin et al., 2007). It includes, for example, solving problems creatively, dealing with uncertain or unpredictable work situations, learning new tasks, technologies, and procedures, and adapting to other individuals, cultures, or physical surroundings.
Griffin et al. (2007) further argued for task proactivity as a separate dimension of job performance. Individual task proactivity reflected the extent to which individuals engage in self-starting, future-oriented behavior to change their work situations, their work roles, or themselves.
Sinclair and Tucker’s job-specific framework (Sinclair and Tucker, 2006) also regarded adaptive performance as a separate dimension of employee job performance, in addition to task performance, contextual performance, and counterproductive work behavior. In several other frameworks, adaptive performance was not included as a separate dimension, but rather as a part of contextual performance. For example, Hunt (1996) dimension of schedule flexibility, Rollins and Fruge (1992) dimension of adaptability, and Hedge, Borman, Bruskiewicz and Bourne (2004) dimension of leading change all reflected an employee’s ability to adapt to new job conditions or requirements.
Operational Framework
Research Hypotheses
Ho1 There is no significant correlation between Hybrid work location and adaptive performance of oil and gas firms in Rivers State.
H02 There is no significant correlation between Remote work location and adaptive performance of oil and gas firms in Rivers State.
Theoretical Framework
Contingency Theory of Management
Robert Blake and Jane Mouton in 1964 suggested that Marx Weber’s bureaucratic theory of management and F. W. Taylor’s scientific management could not succeed because they failed to acknowledge the role of the environment on management style and organizational structure. The different aspects of the environment were seen as the contingent factors. Therefore, they stated that there could be no “one best way” for leadership or organization. William R. Scott described contingency theory as “an organization is best organized depending on the environment with which it deals” (Scott, 1981).
Woodward (1958) also argued that organizational attributes such as span of control, centralization of authority and formalization are directly determined by technologies.
Methodology
Research Design
The research design adopted was cross-sectional survey research design because it is an empirical study research design used to investigate a cause and effect correlation between the independent variable (work location models) and the dependent variable (employee job performance), the research was carried out with the aid a structured questionnaire.
Population of the Study
Population of a study is a set of homogenous elements within a universe that is chosen for a study. The accessible population of this study is 656 administrative staff from five (5) selected oil and gas firms in Rivers State. The accessible population was chosen due to easy accessibility to information and for an unbiased conclusion in order to achieve the aim of the study. The firms and number of employees are given below.
Table 1 Population distribution of research instruments for oil and gas firms
| S/No. | Names of Firms | Number of Admin Staff |
| 1 | Nigerian Liquefied Natural Gas (NLNG) | 70 |
| 2 | Nigerian National Petroleum Corporation (NNPC) | 145 |
| 3 | Halliburton Energy Services Nigeria Limited | 220 |
| 4 | Norfin-offshore Limited | 5 |
| 5 | Shell Petroleum Development Company | 216 |
| Total | 656 |
Source: Human Resource Department of each firm
Sample and Sampling Technique
A sample represents a proportional size of a population that can be handled. The simple random sampling technique, a probabilistic sampling technique will be used in this study. The intention is to get a sample that is convenient to use, accurately represents the population under study because it gives every member of the population an equal chance at being chosen to participate in the survey and it eliminates researcher’s bias in choosing samples. The Taro Yamane’s formula was used for determining the sample size of the study:
n = 2
Where,
n= sample size or population not known
N= population size known
e= error limit given the population (5%)
With the total population being 656 at 95% confidence and error limit of 0.05
The sample size for this study is 248 which were obtained using the Taro Yamane’s formula. However, the Bowley (1964) formula will be used in allocating the questionnaires to each firm. The formula is given as:
nh = nNh
N
nh = The number of questioning distributed to each firm
n = The total sample size
Nh = Number of employees in each firm
N = Population
Nature/Sources of Data
The data used for this study were gotten from primary and secondary sources and the nature of the data is quantitative. The primary data was collected with the aid of structured questionnaires which were administered to respondents via online survey and the secondary data was collected from existing literatures from journals, textbooks, the internet; which provided materials for review of literature and company records; from whence population size was derived.
Methods of Data Collection
The data was collected using electronic survey so as to reach all employees despite the work model they employ i.e. on-site, hybrid and remote. The e-forms were forwarded to employees of the chosen oil and gas firms were requested to forward to as many of their colleagues as possible. The link was also posted on the organisations’ Whatsapp groups by a research assistant from each organisation requesting that employees participate in the survey.
Validity/Reliability of Instrument
Validity refers to the extent to which a research instrument measures that which it ought to measure (Baridam, 2001). Gay (1996) opined that validity is the most important characteristic of a standard test as it measures the accuracy of an instrument. He stated that validity is indispensible and no other test can compensate for inadequacy in validity. The validity test for the research instrument will be conducted thus; it will be presented to the supervisor for vetting and validation, professionals in the field will also be consulted and appropriate corrections will be made.
Reliability is a measure to confirm the extent to which an instrument is consistent. It is the mother of science as science maintains consistency. An instrument is considered reliable when it produces the same result every time it is administered. For reliability test, Cronbach alpha test will be used with a benchmark value of 0.7 along with test and re-test because a pilot survey will be conducted before the main survey.
Data Analysis Techniques
The demographic data will be analysed using descriptive analysis. Spearman Rank Order Correlation Co-efficient statistical analysis will be employed in analysing the hypotheses in order to determine the correlation between the variables with the aid of Statistical Package for Social Sciences (SPSS) and the moderating variable will be analysed using partial correlation. The formula for spearman rank order correlation coefficient is given as;
Rh0 = 1-
where,
Rh0 = Spearman Rank Order Correlation
= Sum of squared difference in the ranking of the two variables
n = Number of subjects being ranked
Analyses and Findings
Ho1 There is no significant correlation between Hybrid work location and adaptive performance of oil and gas firms in Rivers State.
| Correlations | ||||
| Hybrid | Adaptive_performance | |||
| Spearman’s rho | Hybrid | Correlation Coefficient | 1.000 | .581** |
| Sig. (2-tailed) | . | .000 | ||
| N | 248 | 248 | ||
| Adaptive_performance | Correlation Coefficient | .581** | 1.000 | |
| Sig. (2-tailed) | .000 | . | ||
| N | 248 | 248 | ||
| **. Correlation is significant at the 0.01 level (2-tailed). Source: SPSS Output, 2021. |
Our first test of hypothesis reveals a significant relationship between hybrid work and adaptive performance with a correlation coefficient of 0.581 and a p-value of 0.000. With this, we reject the stated null hypothesis and accept the alternate.
H02 There is no significant correlation between Remote work location and adaptive performance of oil and gas firms in Rivers State.
| Correlations | ||||
| Remote | Adaptive_performance | |||
| Spearman’s rho | Remote | Correlation Coefficient | 1.000 | .732** |
| Sig. (2-tailed) | . | .000 | ||
| N | 248 | 248 | ||
| Adaptive_performance | Correlation Coefficient | .732** | 1.000 | |
| Sig. (2-tailed) | .000 | . | ||
| N | 248 | 248 | ||
| **. Correlation is significant at the 0.01 level (2-tailed). |
Source: SPSS Output, 2021.
Our second test of hypothesis reveals a significant relationship between remote work and adaptive performance with a correlation coefficient of 0.732 and a p-value of 0.000. With this, we reject the stated null hypothesis and accept the alternate.
Conclusion
The test results reveal that there is significant correlation between hybrid work and employee adaptive performance. We also reject the null hypothesis and the implication of this could be that when the job is sophisticated and requires modern technology, this creates a sense of satisfaction in workers and makes want to adapt more to the organization. Hilberath et al. (2020) stated that the hybrid work model allows organisations recruit talent better, achieve innovation and create value.
Similarly, our test for hypothesis two reveal there is a significant correlation between remote work and employee adaptive performance. Beno (2021) opined that the positive effects of remote work outweigh the negative. Some positive effects listed include increased employee flexibility, productivity, satisfaction and improvement in WLB. Beno stated that today’s environment is characterized by changes, complexity, interconnectivity and technological development, and new challenges emanate from the external environment which requires flexibility and innovation, as well as the internal environment where employees demand greater autonomy and room for self-expression, hence the need for adoption remote working model by organizations.
Recommendations
Based on the findings and conclusion of this study, the following recommendations are hereby presented:
- Employees should be given the opportunity to participate in decision making to choose models as employee insight and involvement will boost work-life balance and morale as well as commitment to the adopted model, leading to improved job performance.
- Employees will put in more work hours if given the opportunity to work from home and with flexible timing as there will be no boundary between paid work hour and personal time. This will benefit the firms as they need not pay for overtime.
REFERENCES
Abbas, J., Muzaffar, A., Ramzan, M.A. & Ul Hassan Rizvi, S.S. (2014). Impact of Technology on Performance of employees (A Case Study on Allied Bank Limited, Pakistan). World Applied Sciences Journal, 29(2):271-276.
Abdi, N. (2021). 23 Essential Work from Home Survey Questions for Remote of Hybrid Employees. https://sparkbay.com
Agha, S. &Alrubaiee, L. (2011). Effect of Core Competence on Competitive Advantage and Organisational Performance. International Journal of Business and Management, 7(1).
Ajayi, A., Ibeneme, I. &Adediran, I. (2021). Nigerian Oil and Gas Industry Update. KPMG Nigeria. Retrieved from www.mondaq.com
Ajibola, O., Akinlaja, S., Adesoye, E. & Akindele-Oscar, A.(2015). Organisational Variables & Effective Performance of Employees in Oil & Gas Section: An Empirical Investigation. International Jouirnal of Psychological Studies. 7(1).
Alfaleh, A, Alkattan, A., Alageel, A., Salah, M., Almutairi, M., Sagor, K. & Abdulkareem, K. (2021). Onsite versus remote working: the impact on satisfaction, productivity and performance of medical call center workers. Retrived from www.europepmc.org
Bakker, A.B., Demerouti, E. & Verbeke, W. (2004). Using the Job Demands- Resources Model to Predict Burnout and Performance. Human Resource Management, 43(1), 83-104.
Baridam, D.M. (2001). Research Methods in Administrative Sciences. Port Harcourt: Scherbrooke Associates.
Barnard, C. (1938). Functions of Executives: Cooperative Systems. Harvard University Press, United States of America.
Beno, M. &Hvorecky, J. (2021). Data on an Austrian Company’s Productivity in pre-Covid-19 Era, during theLockdown and after Its Easing: to Work Remotely or Not? Frontiers in Communication, 6(641199), 1-10
Burton, W.N., Pransky, G., Conti, D.J., Chen, C.Y. & Edington, D.W. 2004. The association of medical conditions and presenteeism. Journal of Occupational and Environment Medicine, 46, 38–S45.
Campbell, J.P. Dunnette, M.D. & Lough, L.M.(1990). Modeling the Performance Prediction Problem in Industrial and Organizational Psychology. In Handbook of Industrial and Organizational Psychology. American Psychological Association: Worcester, MA, USA.
Capello, R. (2014) “Classical Contributions: von Thünen, Weber, Christaller, Lösch,” in Manfred M. Fischer & Peter Nijkamp (editors), Handbook of Regional Science. Springer:New York, pp. 507–526.
Eurofound and the International Labour Office (2017).Working anytime, anywhere: The effects on the world of work. Publications Office of the European Union, Luxembourg, and the International Labour Office, Geneva. Retrieved from Eurofound Publications website: https://www.eurofound.europa.eu/publications/
Ferreira, J., Claver, P., Pereira, P. & Thomaz, S. (2020). Remote Working and the Platform of the Future. Boston Consulting Group. Retrieved from www.bcg.com
Fluegge-Woolf, E.R. (2014). Play Hard, Work Hard. Management Research Review, 37(8), 682-705.
Humphrey, A. (2005). “SWOT Analysis for Management Consulting”. SRI Alumni Newsletter. SRI International.
Hunt, S.T. (1996). Generic Work Behavior: An Investigation into the Dimensions of Entry-Level, Hourly Job Performance. Personal Psychology, 49:51–83.
Kozolowski, S.W.J. & Bell, B.S. (2003). Work Groups and Teams in Organisations. Handbook of Psychology: Industrial and Organisational Psychology, 12, 333-375.
Kramer, R. (1991). Intergroup Relations and Organisational Dilemmas: The role of categorization processes. Research in organizational behaviour. L.L. Cummings & B.M. Staw (Editors) 13, 191-228.
Krugman, Paul. (1991) “Increasing Returns and Economic Geography,” Journal of Political Economy, 99, 493–499.
Lance, C.E., Teachout, M.S. & Donnelly, T.M. (1992). Specification of the Criterion Construct Space: An Application of Hierarchical Confirmatory Factor Analysis. Journal of Applied Psychology, 77(4), 437-452.
Law, M.M.S., Hills, P.,Hau, B.C.H. (2017).Engaging employees in sustainable development–a case study of environmental education and awareness training in Hong Kong. Business Strategy Environment, 26, 84–97.
Opperman C. S. (2002).Tropical Business Issues. Partner Price Water House Coopers. International Business Review.
Ozcelik, G. & Uyargil, C. (2016). High Performance Work Systems and Organisational Values: Resource-based View Considerations. Procedia- Social and Behavioural Sciences 235, 332-341.
Pavalache-Ilie, M.(2014). Organizational citizenship behaviour, work satisfaction and employees’ personality. Scientific Direct Procedia of Social Behaviouural Science.
Simon, H. (1947). Administrative Behaviour: The Concept of Systems Process in Decision-making within the Organisation. The Macmillian Company, United States of America.
Sinclair, R.R., Tucker, J.S. (2006). Stress-CARE: An Integrated Model of Individual Differences in Soldier Performance under Stress. In: Britt TW, Castro CA, Adler AB, Eds. Military Life: The Psychology of Serving in Peace and Combat 1, Military Performance. Westport, CT: Praeger Security International, 202–231.
Velasquez, M., Andre, C., Shanks, J.S. and Meyer, M.J. (2010). What is Ethics? Markkula Center for Applied Ethics at Santa Clara University. Retrieved from www.scu.edu
Viswesvaran, C. (2002). Assessment of Individual Job Performance: A Review of the Past Century and a Look Ahead. Handbook of Industrial, Work and Organisational Psychology, 1, 110-126.
Zheng, W., Kaur, S. &Zhi, T. (2010). A Critical Review of Employee Turnover Model (1938-2009) and Development in Perspective of Performance. African Journal of Business Management, 4(19), 4146-4158.
