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Article

Adopting Technological Innovations in the Air Cargo Logistics Industry in South Africa

by
Adedotun Joseph Adenigbo
*,
Joash Mageto
and
Rose Luke
Department of Transport and Supply Chain Management, University of Johannesburg, Johannesburg 2006, South Africa
*
Author to whom correspondence should be addressed.
Logistics 2023, 7(4), 84; https://doi.org/10.3390/logistics7040084
Submission received: 30 August 2023 / Revised: 12 October 2023 / Accepted: 9 November 2023 / Published: 16 November 2023
Versions Notes

Abstract

:
Background: The air cargo logistics industry has been deemed reluctant to adopt new technologies for their business operations. So, this study aims to examine the adoption of technological innovations in the air cargo logistics industry in South Africa. The specific objective is to emphasise the effects of technologies on air cargo logistics operations to address the reluctance to adopt technological innovations in the industry. Methods: A questionnaire survey was conducted randomly on three hundred and seventy-three (373) cargo agents at the OR Tambo International Airport. The data were subjected to a nonparametric test with Kruskal–Wallis, exploratory factor analysis and regression analysis to explore the effects of technologies for prompt adoption and implementation of emerging innovations that enhance quality service delivery. Results: The study found that promptly adopting emerging technological innovations in the air cargo logistics industry promotes efficient operations, improves warehousing, and enhances cargo delivery services for customer satisfaction. Also, the study established that technologically driven operations and warehousing are significant determinants of quality service delivery in the air cargo logistics industry. Conclusions: This study encourages the prompt adoption and implementation of technological innovations for improved quality service delivery, customer satisfaction, and loyalty in the air cargo logistics industry.

1. Introduction

The air cargo logistics industry is part of the services offered in the aviation sector. The industry fulfils a segment of the supply chain system for the seamless distribution of goods to meet consumers’ demands. This study defines air cargo as goods carried by aircraft, whether chartered or commercial, under customs-bonded airway bills. Many scholars, such as [1], have referred to air cargo as a by-product of passenger traffic, where airlines traditionally load cargo into the belly holds of their passenger aircraft. Air cargo operation is observed as an efficient means of transportation because of its capacity to handle high-valued and time-sensitive goods [2,3]. However, the turn of global events resulting from the COVID-19 pandemic and the booming e-commerce demand have reshaped the outlook of the air cargo logistics industry for the future. The International Air Transport Association [4] emphasised how e-commerce accelerates transformation in the global air cargo logistics industry with 15% of volume in 2019 and 18% of total retail sales in 2020.
Ref. [5] compared cargo volume for February 2019 with February 2018 and stated that the air cargo industry experienced a weakened demand, with a fall of 4.7%. Ref. [5] also reported that the air cargo market capacity increased by 2.7% yearly. The record shows that it was the slowest annual growth rate in three (3) years. In contrast, other studies had anticipated that air freight volumes would continue to increase by around 3% annually until 2025 and likely after 2030 [6]. According to [7], air cargo traffic will increase by 4.3% annually for the next 20 years.
Recent technological advances are poised to impact air cargo operations for efficient supply chain management to ensure the seamless distribution of goods with automated coordination of air cargo flow. For stakeholders in the logistics industry, digitisation with ICT and IoT is becoming unavoidable [8] for seamless supply chain operations in air cargo distribution. The requirement to adopt new technological innovations has prompted severe concerns for stakeholders in the air cargo logistics industry. The concerns border on the reluctance for the prompt adoption and implementation of emerging innovations in the industry due to the cost of technological acquisition and negative attitude to change. Nevertheless, the impact of technology on industries worldwide cannot be overemphasised. Its influence is highly significant to have transformed how companies conduct their businesses. The air cargo industry is no exception, as emerging technological innovations are fast becoming major driving forces. Thus, stakeholders in the air cargo logistics industry are under severe pressure to digitise their current operations to remain relevant due to the increasingly widespread technological innovations [4].
Notwithstanding, the reluctance to adopt and implement emerging technological innovations in the air cargo logistics industry prevails [9]. As a result, the air cargo industry is often lamented as lagging in digitisation behind passenger air transport [10]. The air cargo industry is slow to adopt digitisation to modernise distribution although technological applications are required for data and cargo management systems across all distribution stages [11,12]. Similarly, ref. [13] stated that adopting new innovative technologies continues to be slow and less than optimal across organisations. The traditional human behaviour to resist change [14] and the huge cost of acquiring new technologies account for the attitudinal reluctance of air cargo stakeholders to adopt and implement new technological innovations promptly. So, this study identifies and emphasises the effects of technological innovations in improving air cargo service delivery with the aim of addressing the reluctance or slowness in adopting technological innovations in the air cargo logistics industry.
To this end, this study aims to provide convictions for the prompt adoption of technological innovations in the air cargo logistics industry by identifying the significant effects of technologies on air cargo service delivery among stakeholders in South Africa. The focus is to reduce the reluctance to adopt technological innovations created to facilitate the seamless distribution of cargo for economic development in South Africa. This paper’s main question is as follows: what is the effect of adopting emerging innovative technologies in the air cargo logistics industry in South Africa? The goal is to address the level of reluctance and slowness of the industry to adopt and implement emerging technologies for handling air cargo operations. The study contributes to the existing knowledge by emphasising the effects of technological applications in the air cargo logistics industry to promote attitudinal change for prompt acceptance, adoption, and implementation of emerging technological innovations. Importantly, this study provides evidence to persuade prompt adoption of technological innovations in the air cargo logistics industry. The need for prompt adoption of advanced and emerging technologies in the air cargo logistics industry is to address problems such as the disintegration of the supply chain system [15]. The authors of [16] also highlighted other problems, such as cost-ineffectiveness, the prevalence of paperwork, and limited data management systems. The problems remain prevalent in the industry when emerging technologies can accelerate progress and transform the landscape for efficient air cargo service delivery. So, the study argues that adopting technological innovations in the air cargo logistics industry offers efficient operations for quality customer service delivery.
The literature on adopting technological innovations has focussed on the airline subsector of the air transport industry in South Africa. Such studies include [17], self-service bag drops [18], and airlines [19]. Very little research has been conducted on technological innovations in the air cargo logistics industry. So, this study focuses on the air cargo logistics industry, which is yet to have in-depth research studies on adopting technological innovations in South Africa.
The paper is structured such that after this introductory section, Section 2 provides the review of the literature, Section 3 dwells on the method used for the study, Section 4 presents the results of the analysis, Section 5 provides a detailed discussion of the results, and Section 6 concludes the study with policy implications and future study.

2. Literature Review

Over the years, much research has been conducted on airlines and passenger traffic at airports. However, most researchers consider air cargo a subsidiary of passenger operations, and airlines mostly carry cargo in the belly of their passenger aircraft. However, the recent COVID-19 pandemic revealed the importance of cargo to airlines and airport operations beyond its consideration as a by-product of passenger traffic. The ban on passenger movement during the pandemic highlighted that cargo traffic could substantially make airline and airport operations. So, according to [20], studies that address different problems in the air cargo industry focus on the airline, freight forwarders, and supply chain perspectives. The studies reviewed by [20] have none that focused on technological innovations in the air cargo industry. Notwithstanding, the empirical review for this study explores the effects of technological applications in the air cargo logistics industry and highlights the knowledge gap about the scarcity of studies that address the reluctance to adopt technological innovations in the industry, especially in South Africa.

2.1. Effects of Technological Applications in the Aviation Logistics Industry

The logistics industry is broad, with studies focusing on the need to apply technological applications in different aspects of operations such as RFID adoption [21], technology forecasting framework [22], inventory management [23], humanitarian logistics [24], digital marketing [25], digitisation [26], automation of warehousing [27,28], supply chain management [29], and [30] explored the implications of adopting industry 5.0 for smart logistics, etc.
In the aviation sector, most studies focus on the effects of technological innovations on airline operations. For example, ref. [31] found that technologies significantly influence air transport’s safety, efficiency, capacity, capability, environment, and financial outlook. Ref. [32] also found that digital technology applications impact airline operations by enhancing passengers’ flight experience. Ref. [33] stated how technological innovations have helped reshape the aviation industry by making it more efficient, safe, sustainable, and agile. Ref. [34] examined the effects of innovative technologies to provide comfortable, smart, and safe air travel in Asia. The passengers’ perception of technological innovation and adoption during the COVID-19 pandemic was assessed by [35] in China and emphasised the effects of technological adoption on improved airline service delivery.
For the air cargo industry, there is a scarcity of studies focussing on the problems of adopting technological innovations in the air cargo logistics industry. However, studies have also emphasised the effects of technological innovations in handling and distributing cargo. For example, ref. [36] stated that emerging technological innovations have become an important part of cargo delivery for integrating supply chain operations due to globalisation. Ref. [37] affirmed that technological applications in air cargo supply chain management serve as a digital solution to simplify distribution processes. Ref. [38] also highlighted the effect of applying digital technologies in the cargo shipping business. The effect of applying blockchain technology in utilising airport resources for optimal service delivery was identified by [37]. Information technology infrastructure can provide a platform for all agents in the air cargo industry to collaborate online for service delivery [12].
So, it is apparent from the literature that technological applications are important to the air cargo logistics industry for seamless distribution with increased effectiveness in real-time delivery. Despite the importance of technological innovations to the air cargo logistics industry, to the best of our knowledge, no literature addresses the reluctance to adopt emerging technologies promptly. This supports the claim of [39] that empirical studies on the effects of 4IR adoption in the airline industry in South Africa are scarce.
Specifically, empirical studies that relate technological innovations and their effects on air cargo operations are lacking in South Africa. It implies that researchers in South Africa have not considered the benefits of the air cargo industry to the country’s development. Ref. [39] recently examined the effects of 4IR technologies adoption in the South African airline industry on the dynamics in the labour markets. Also, ref. [19] examined the surviving strategies of the South African airline industry with the adoption of smart technologies. The studies were devoted to the airline industry and imply the need to fill a knowledge gap about the effects of adopting technological innovations in the air cargo logistics industry in South Africa.
Hence, stakeholders in the air cargo industry need to be persuaded to adopt and implement emerging technologies for their operations promptly. So, this study presents the effects of adopting and implementing emerging technological applications in the air cargo logistics industry to encourage the timely deployment of technological innovations.

2.2. The Air Cargo Industry in South Africa

The air cargo industry in South Africa is versatile and contributes substantially to the growth and development of the country. South Africa has the United Arab Emirates, United Kingdom, Kenya, Qatar, and Germany as the busiest air cargo routes [40]. The air freight industry in South Africa generated a huge sum of 5.1 billion Rands in 2016 to contribute substantially to the country’s economic growth [41]. In 2021, the South African air cargo industry had a total revenue of USD 0.2 billion [42]. South African airports’ cargo traffic ranked second after Egypt in the African continent [43]. Three (3) major implications for the air cargo industry in South Africa are: 1. there is major air trade and exchange between South Africa and other countries in Asia, Europe, and within the African continent; 2. the air cargo industry significantly contributes to the annual revenue and growth of South Africa; and 3. South African air cargo traffic is substantial for trade and exchange in Africa.
The implications indicate the importance of the air cargo logistics industry to economic development. It also shows the connectivity of the distribution of goods across the world’s continents for global trade facilitation. It finally reflects the importance of cargo traffic to airport and airline operations. The implications provide the need for studies that focus on the air cargo logistics industry in South Africa, as it not only contributes to the country’s development but also influences the global trade relationship and promotes airports and airline operations.

3. Methods

This study adopted the survey research design for data collection and analysis. The research design is exploratory. The philosophy behind the research is positivism, which aims at quantifying data for exploratory analysis used in this study. The research study area was O. R. Tambo International Airport (OR Tambo), Johannesburg. O.R. Tambo is a major airport regarding cargo volume and traffic in Africa, with substantive cargo traffic in the continent. O.R. Tambo International Airport leads the remaining twenty-one (21) airports as the largest and busiest airport in South Africa. According to the Airports Council International (ACI) data, OR Tambo International Airport was ranked third (3rd) with 304,018 Tonnes in African cargo handling after Cairo and Jomo Kenyatta international airports [44].
The study used cargo agents, also known as freight forwarders, as the population for the research. The employees of the cargo agents’ companies with offices at the Agent Building of the airport’s cargo terminal form the study population. In all, ninety-seven (97) companies were contacted for this study. The study purposively sampled five (5) from each company to a total of four hundred and eighty-five (485) population. The selection aims to ensure that the general manager/manager, two partners and two other employees were included from each company in the survey. The simple random sampling technique was adopted to administer a questionnaire to the respondents. The random sampling technique was adopted for its capacity to provide an equal chance for each member of the population to be sampled. Random numbers using MS Excel were generated and assigned to each questionnaire before administration.
A questionnaire was designed to collect data on the effects of the adoption of advanced technological applications in the air cargo logistics industry in South Africa. The choice of using a questionnaire to collect data for the study is because of its ability to structure and standardise responses that can be quantified for statistical analysis. The questionnaire was administered with the assistance of four (4) research assistants, who were duly trained to collect the data for the study. The questionnaire contains two (2) sections. Appendix A.1 seeks the respondents’ background information, and Appendix A.2 seeks the opinion of the respondents about the effects of adopting advanced technological applications in air cargo logistics in South Africa. The initial questions in Appendix A.2 attempt to establish the stakeholders’ responsiveness to adopt technological innovations. The other questions were presented on a 5-point Likert Scale about the effects of adopting technological applications in the air cargo logistics industry for the respondents to consider in order of significance. The order follows 1—Not Significant to 5—Highly Significant. The variables for the study were extracted from [45] the measurement framework for assessing disruptive innovations. The questionnaire used to collect data for the study is attached as Appendix A.
The questionnaire contains preliminary questions highlighting stakeholders’ responses and the adoption of technological applications in the air cargo industry. For the main issue of the study, the questionnaire presents eighteen (18) items in the 5-point Likert scale for respondents to rank in order of significance. The items are the effects of technological applications in logistics operations. The items include integrated operations, efficient operations, reluctance to adopt, improved operations, technology diffusion, simplified handling process, reduced shipping time, order accuracy, reduced transport cost, inventory accuracy, reduced clearing time, improved warehousing operations, equipment utilisation, forecast uncertainty, monitor demand variability, improve cargo supply, and delivery within time. The items were extracted from literature such as [6,31,32,36,37,38]. The questionnaire was administered to cargo agents and returned three hundred and seventy-three (373) copies of the questionnaire with valid responses. The sample size had a 76.9 percent success rate in the data collection process.
Firstly, the data collected with the initial questions in part B of the questionnaire were analysed using Kruskal–Wallis to test if there is a significant reluctance to adopt technological innovations in the air cargo logistics industry in South Africa. The data for the remaining questions in Part B of the questionnaire were subjected to Exploratory Factor Analysis (EFA) for dimension reduction. The goal of the technique is to reduce the variables to a few of the most significant ones that best explain the effects of adopting emerging technological innovations in the air cargo logistics industry in South Africa. EFA determines whether reduced unobserved common factors can represent the observed covariances or correlations in the variables. The technique estimates the factor loadings of the items to determine the number of common factors that can adequately describe the correlations between the observed variables [46].
Principal Axis Factoring (PFA) was employed in implementing EFA. For a decision on the correctness of the analytical output, the procedure for conducting EFA statistically includes techniques using Kaiser–Meyer–Olkin (KMO) and Bartlett tests, communalities, extraction of variance using principal axis factoring, correlation matrix, and factor rotation (with varimax) technique [47].
The final output of the EFA identified efficient operations, warehousing operations, and improved service delivery as the most significant effects of adopting emerging technological innovations in air cargo logistics. This takes the analysis further by subjecting the three identified effects to regression analysis. The idea is to determine how efficient operations and improved warehouse operations contribute to service delivery in the industry. Service delivery is chosen as the dependent variable because the goal of developing technological innovations is to enhance service delivery for increased customer satisfaction. As customer satisfaction enhances customer loyalty, it is ideal that it will convince stakeholders in the air cargo logistics industry to adopt emerging technological innovations promptly.
The regression model was built to determine the extent to which efficient operations and warehousing affect service delivery in the air cargo logistics industry in South Africa. The data for the regression analysis were generated by transforming the items that load on each of the common factors extracted by EFA into new variables by their mean values.
The model was represented as follows.
Y = a + β 1 X 1 + β 2 X 2 + ε
where
  • Y = Service Delivery (dependent variable);
  • a = Constant;
  • X1 = Efficient Operations (independent variable);
  • X2 = Warehousing (independent variable);
  • β = Coefficient of the independent variables;
  • ε = Error term.
So, the model for this study takes the form.
S e r v i c e   D e l i v e r y = a + E f f i c i e n t   O p e r a t i o n s ( X 1 ) + W a r e h o u s i n g ( X 2 ) + ε
The coefficients of the variables were determined in their unstandardised form to determine the influence of improved warehousing and efficient operations on service delivery in the air cargo logistics industry in South Africa.

4. Results

The study applied the Kruskal–Wallis test to determine the existence of reluctance to adopt and implement new technological innovations in the air cargo logistics industry in South Africa. Table 1 shows the mean ranks and test statistics to indicate if there is a significant reluctance to promptly adopt emerging technological innovations in the air cargo logistics industry in South Africa. Table 1 shows that the highest mean rank of 329.87 is associated with “Least Responsive/Adopted”. The test statistics indicate that a Kruskal–Wallis value of 219.920 is significant, with p < 0.000 for the response to the adoption of technological innovations in the air cargo logistics industry in South Africa. The significant value (p = 0.000) implies that the null hypothesis stating that there is no reluctance to the adoption of technological innovations in the air cargo logistics industry in South Africa is rejected. It implies that there is low responsiveness to the adoption of emerging technological innovations in the air cargo logistics industry in South Africa.
The output in Table 2 shows that the data is suitable and adequate for the analysis with the results of KMO and Bartlett’s tests. The KMO test showing 0.584 indicates that the sample is adequate for EFA. Also, Bartlett’s test of sphericity is significant at 0.000 with an approximate Chi-Square value of 2665.901, showing that the data is suitable for the exploratory factor analysis (See Table 2). This study considered the KMO value of 0.58 with Bartlett’s test at p = 0.000 as appropriate, following the suggestion of [48] that a KMO over 0.5 has a substantial correlation for data suitability for EFA. Also, ref. [49] stated that a KMO greater than 0.5 is considered suitable for EFA. According to the sample size, ref. [50] suggested that a KMO value between 0.5 and 0.6 is acceptable for a sample size above 100.
The communalities estimate of the data presented in Table 3 shows that the variance in the variables can be ascribed to the common factors. The variance of each variable estimated by the analysis makes the communalities of the data. The model’s communalities represent the sum of the squared loadings of the initial and extracted values of the variance of each variable. The extracted values presented in Table 3 show that all the variables have an acceptable variance to explain the variability in the common factors that serve as the effects of adopting advanced technological applications in the air cargo logistics industry in South Africa.
The items with values less than 0.400 after extraction may not contribute significantly to the reduced factors that represent the effects of adopting technological innovations in the air cargo logistics industry in South Africa. The items are address reluctance (0.275), reduced shipping time (0.433), and inventory accuracy (0.389). The varimax rotation of the analysis will produce values that determine the contribution of each of the items to the common extracted factors.
The analysis took critical steps to identify the number of common factors that represent the effects of adopting technological innovations in the air cargo logistics industry in South Africa. The model specifies that the number of common factors is restricted to a maximum of three (3). So, the extraction was conducted using principal axis factoring with varimax rotation to identify the latent variables that make the common factors. The values were rotated according to size and suppressed to the least coefficient of 0.500. The percentage of the total variance that explained the common factors accounts for 59 per cent of the factors serving as the effects of adopting technological innovations in the air cargo logistics industry in South Africa (see Table 4).
Table 4 provides an understanding of the proportions of the variance in the variables that make each common factor that serves as the effect of adopting technological applications in the air cargo logistics industry in South Africa. The percentage of the eigenvalues explains the total variation in the data that a factor represents.
The final output of the analysis is the rotated factor matrix presented in Table 5. It indicates the three (3) major effects of adopting advanced technological innovations in the air cargo logistics industry in South Africa. The major items of the data are well loaded on each extracted factor, serving as the major effects of adopting technological innovations in the air cargo logistics industry. The loadings of each item on the factors show that items “reduce shipping time” and “address reluctance” have values less than 0.500 and have no contribution to the common factors serving as the effects of adopting advanced technological innovations in the air cargo industry in South Africa.
A critical examination of the result in Table 5 shows the correlated values of each item on the common factors. The items with the highest loading factors on each of the common factors have an efficient operation (0.780) on Factor 1, warehousing operations (0.767) on Factor 2 and improved service delivery (0.769) on Factor 3. Following the highest values of the items, the significant effects of adopting emerging technological innovations in the air cargo logistics industry in South Africa were named efficient operations and improved warehousing operations for factors 1 and 2. Factor 3 was named improved service delivery.
The study further analysed the data using a regression model to determine the relationships between the extracted common factors serving as the effects of adopting technological innovations in the air cargo logistics industry in South Africa. The analysis aims to determine the extent to which the adoption and application of technological innovations enhance service delivery in the air cargo logistics industry. The model summary in Table 6 shows that the R Square value of 0.365 indicates a strong relationship between the variables. It indicates that 36.5% of the quality service delivery in the air cargo logistics industry can be explained by efficient operations and improved warehousing derived from adopting and implementing emerging technological innovations.
The ANOVA test of the significance of the data for regression analysis shows F = 106.273 at p = 0.000. (See Table 7). The result implies that the effects of adopting technological innovations in the air cargo logistics industry will enhance service delivery. The test has quality service delivery as the dependent variable, while efficient operations and improved warehousing were the independent variables. The hypothesis tests if efficient operations and improved warehousing significantly influence quality service delivery in the air cargo logistics industry. Table 7 shows a significant relationship between quality service delivery, efficient operations, and improved warehousing as the effects of adopting technological innovations in the air cargo logistics industry.
The coefficient of the effects of adopting technological innovations in the air cargo logistics industry presented in Table 8 shows the regression coefficient estimates that determine the extent of the relationship between quality service delivery, efficient operations, and improved warehousing that technological innovation enhances. The model’s estimated coefficients for this study are provided under “Unstandardised Coefficients B”. The coefficients indicate the predicted change that each explanatory variable has in the dependent variable. The estimates are determined when each variable is increased by a unit conditional upon all the other variables remaining constant. Therefore, the effect of adopting technological innovations in the air cargo logistics industry quality service delivery will tend to increase by 49.8% and 24.4% for every unit increase in warehousing and efficient operations.
The VIF statistics of the model indicate collinearity level in the analysis. The tolerance values of 0.608 are greater than 0.100 to imply that there is no multicollinearity in the model. The VIF values of 1.644, respectively, indicate that the variables are not correlated. Hence, the analysis output is assumed to be reliable for informed policy decisions.
So, the model for the study is defined as
S e r v i c e   D e l i v e r y = 0.842 + E f f i c i e n t   O p e r a t i o n s ( 0.244 ) + W a r e h o u s i n g ( 0.498 ) + ε
The model predicts that improved warehousing and efficient operations will guarantee quality service delivery from the benefit of adopting technological innovations in the air cargo logistics industry. It implies that applying technological innovations in the air cargo logistics industry improves service quality delivery for customer satisfaction.

5. Discussion

Logistics challenges can be addressed by streamlining the supply chain connected by data platforms from order fulfilment to uninterrupted and optimal cargo distribution for efficient service delivery. This will mean providing a distribution system with a reduced delivery cost at optimal loading delivery capacities for customer satisfaction. The goal is to make technology support the flow of goods such that customers can trace their goods throughout the delivery process, even in the air, for maximum service satisfaction. This study has found that the major effects of adopting emerging technological innovations in the air cargo logistics industry are providing the lead for efficient operations, improving warehousing operations, and supporting quality service delivery. The ultimate finding of the study is that the application of technological innovations leads to quality service delivery in the air cargo logistics industry.
Each factor is believed to singly or jointly significantly affect the air cargo logistics industry in any country. The operational efficiency of cargo is pivotal to supply chain management and can drive customer-focused services in the air cargo industry. The efficiency in operations regarding air cargo logistics focuses on examination, clearing, handling costs and seamless distribution within the supply chain system. The application of technology provides efficiency by reducing the volume of manual handling of processes in the distribution management of cargo by the least waste of time and effort. According to [51], the efficiency of cargo supply operations via technological applications drives service quality with automation, diversification, collaboration, and digitisation. The finding of this study about operational efficiency supports the conclusion of [6] that implementing technological applications and innovations in the air cargo logistics industry has brought significant improvements in operational efficiency with electronic air waybills for quality service delivery. It implies that the prompt adoption of emerging technological innovations in the air cargo logistics industry is important because of its capacity to enhance service delivery and customer satisfaction.
Warehousing has remained a significant aspect of the supply chain system for distributing and delivering goods from the origin to the destination. The warehouse plays an intermediary role in handling goods for delivery. It indicates that improved warehousing operations are required for satisfactory customer service in the air cargo logistics industry. This study found that technological innovations for warehousing operations significantly affect air cargo logistics in South Africa. This corroborates [52] that warehousing technology boosts air cargo logistics operations by reducing errors and costs for improved customer service delivery. Also, ref. [53] stated that technology applications contribute to warehousing operations with improved inventory management for the logistics industry. The technological innovations in the air cargo logistics industry enhance the innovative warehouse operations management for information connectivity, process automation and sustainability [54]. This study provides insight into the need to adopt emerging innovative warehouse technologies in the air cargo logistics industry for quality service delivery in the air cargo logistics industry.
The hallmark of the result is that applying technologies in the air cargo logistics industry in South Africa will significantly improve service delivery to customers. This will be achieved by supporting the timely delivery of goods to customers. It implies that technological innovations in the air cargo logistics industry enhance customer satisfaction due to the time and cost reduction in cargo delivery. The result also supports [55], who found the need for air cargo service improvement via technological applications for payment transactions and airline product promotion to increase customer loyalty. According to [56], the air cargo service quality technology sections include customer service, import and export, project management, administration, and warehousing. Thus, technological innovations in the air cargo logistics industry improve service delivery for customer satisfaction in the air cargo logistics industry in South Africa. Adopting and applying technological innovations in the air cargo logistics industry ultimately enhances quality service delivery and customer satisfaction. Customer loyalty and patronage for air cargo services will increase with the adoption and application of technological innovations in the air cargo logistics industry. So, the need for prompt adoption and implementation of technological innovations is important for quality service, customer satisfaction, and loyalty.

6. Conclusions and Recommendations

Technological advancements have continued to affect industries in magnificent ways. However, the response rate to adopting and implementing emerging technological innovations results from the need to determine the outcome of technological applications in most industries. This study attempts to highlight the significant effects of technological innovations to address the reluctance to adopt emerging technologies in the air cargo logistics industry in South Africa. The study data were collected with a questionnaire administered to freight forwarders to attach a level of significance to each of the items that make the effects of technology in the air cargo logistics industry. The data were subjected to the Kruskal–Wallis test, exploratory factor analysis and regression analysis. In summary, the study found that applying technological innovations in the air cargo logistics industry promotes efficient operations, improves warehousing, and enhances cargo service qualities for customer satisfaction. Furthermore, the study established that technologically driven operations and warehousing are significant determinants of quality service delivery in the air cargo logistics industry. This emphasises the need for a prompt response to adopt and implement emerging technologies in the air cargo logistics industry to deliver high service qualities for customer satisfaction and loyalty.
The findings imply that applying technological innovations in the air cargo logistics industry improves operations for efficient distribution, warehousing operations for efficient handling, and service delivery for improved customer satisfaction, patronage, and loyalty. With increased customer satisfaction, patronage and loyalty, stakeholders in the industry will experience increased demand and profit. Although adopting and implementing technological innovations will increase costs (technological acquisition costs) in the short run, the profit margin will justify the costs in the long run. The profit will be derived from the increased customer demand that technological innovations enhance. So, the finding dissuades the behavioural fear of change and encourages prompt investment in acquiring new technologies for air cargo operations. Therefore, prompt responsiveness to adopt emerging technological innovations in the air cargo industry should be expected from stakeholders.
The study recommends a positive attitudinal change to prompt the adoption and implementation of emerging technological innovations in the air cargo logistics industry. This is necessary because of the effects of technological innovation to render efficient operations, improved warehousing, and quality service delivery functions to provide customer satisfaction and loyalty that leads to increased customer patronage and profit.
In conclusion, this study provides a persuasion for the prompt adoption of technological innovations with the fact that managers will enjoy higher profit margins with the effects of technological innovations in the air cargo logistics industry in South Africa. However, the government may adopt the action points by [13] on incentives for technological adoption and encourage investment in research and infrastructure for technological development in the air cargo industry.

7. Contribution of the Study

The study’s findings provide a basis for the prompt adoption and implementation of emerging technological innovations in the air cargo logistics industry, thereby eliminating the industry’s current reluctance to adopt technological innovations. The outcome also adds to the body of knowledge on the effects of technological innovations in any economic sector.

8. Limitations and Future Research

Notwithstanding the generalisation of the study findings, it is limited as follows:
  • The study specifically considered the air cargo industry in South Africa. Future studies need to examine the factors in other countries.
  • The study does not assess the development of technological applications for the air cargo logistics industry.

Author Contributions

Conceptualization, A.J.A.; methodology, A.J.A.; validation, A.J.A.; formal analysis, A.J.A.; writing—original draft preparation, A.J.A.; writing—review and editing, A.J.A., J.M. and R.L.; supervision, J.M. and R.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors acknowledge the respondents and research assistants.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Questionnaire

Adopting Technology Innovations in the Air Cargo Logistics Industry in South Africa
Dear Sir/Ma,
This questionnaire is designed to obtain information for research purposes at the University of Johannesburg, South Africa. Your support is solicited to provide adequate and accurate information to the questions, as all information provided will be treated with utmost confidence.

Appendix A.1. Demographic Information

  • Gender: Male ( )1 Female ( )2
  • Age: ………………….
  • Marital Status: Married ( )1 Single ( )2 Divorced ( )3
  • Educational Status: Primary Education ( )1 Secondary Education ( )2 Tertiary Education ( )3
  • Years of Work Experience: ………………………………
  • Name of your company (Cargo Agency) …………………………………………….
  • Your role in the company: General Manager/Manager ( )1 Partner ( )2 Other Employees ( )3

Appendix A.2. Effect of Technological Innovations in the Air Cargo Industry in South Africa

8.
What is your view about the response of stakeholders to technological innovations in the air cargo industry in South Africa?
Highly Responsive ( )1 Moderately Responsive ( )2 Least Responsive ( )3 Not Responsive ( )4
9.
What is your view about the level of adoption of new technologies in air cargo logistics operations in South Africa?
Highly Adopted ( )1 Moderately Adopted ( )2 Least Adopted ( )3 Not Adopted ( )4
10.
Would you agree that technological applications affect the operations in the air cargo industry in South Africa? a. Yes, Agreed ( )1 b. No, Disagreed ( )2
11.
If “Yes” to question 10, can you please rank the items presented in the table below to assess the effect of technological applications in the air cargo industry in South Africa in order of significance from 1—Not Significant to 5—Highly Significant.
S/NFactors1—Not Agreed2—Least Agreed3—Fairly Agreed4—Agreed5—Highly Agreed
1Integrated Operations
2Efficient Operations
3Address Reluctance
4Improved Operations
5Technology Diffusion
6Simplified Handling Process
7Reduced Shipping Time
8Order Accuracy
9Reduced Delivery Time
10Reduced Transport Cost
11Inventory Accuracy
12Reduced Clearing Time
13Improved Warehousing Operation
14Equipment Utilisation
15Forecasts Uncertainty
16Monitor Demand Variability
17Contribute to Cargo Supply
18Delivery within Time

References

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Table 1. Kruskal–Wallis test of responsiveness to adopt technological innovations.
Table 1. Kruskal–Wallis test of responsiveness to adopt technological innovations.
Adoption of Technological InnovationsNMean Rank
Responsiveness to adopt Technological Innovations1—Highly Responsive/Adopted201126.35
2—Moderately Responsive/Adopted102196.61
3—Least Responsive/Adopted55329.87
4—Not Responsive/Adopted15310.50
Total373
Test Statistics a,b
Responsiveness to Adopt TI
Kruskal–Wallis H219.920
Df3
Asymp. Sig.0.000
a. Kruskal–Wallis Test; b. Grouping Variable: Adoption of TI.
Table 2. Tests of data adequacy and suitability for EFA.
Table 2. Tests of data adequacy and suitability for EFA.
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.0.584
Bartlett’s Test of SphericityApprox. Chi-Square2665.901
Df153
Sig.0.000
Table 3. Communalities of variables.
Table 3. Communalities of variables.
InitialExtraction
Integrated Operations0.9680.618
Efficient Operations0.9730.688
Address Reluctance0.6530.275
Improved Operations0.8920.545
Technology Diffusion0.8530.445
Simplified Handling Process0.8730.683
Reduced Shipping Time0.7160.433
Order Accuracy0.7670.512
Reduced Delivery Time0.7410.601
Reduced Transport Cost0.9340.700
Inventory Accuracy0.6950.389
Reduced Clearing Time0.6800.367
Improved Warehousing Operations0.9610.829
Equipment Utilisation0.8560.600
Forecasts Uncertainty0.9470.810
Monitor Demand Variability0.8310.719
Contribute to Cargo Supply0.9380.747
Delivery within Time0.8790.686
Extraction Method: Principal Axis Factoring.
Table 4. Total variance explained for adopting technological innovations.
Table 4. Total variance explained for adopting technological innovations.
FactorInitial EigenvaluesExtraction Sums of Squared LoadingsRotation Sums of Squared Loadings
Total% of VarianceCumulative %Total% of VarianceCumulative %Total% of VarianceCumulative %
18.07444.85844.8587.73242.95442.9544.05622.53122.531
21.97910.99455.8521.5728.73351.6873.47919.32841.859
31.85210.28766.1381.3437.46159.1483.11217.28959.148
41.0435.79571.933
51.0245.68877.621
60.8344.63482.255
70.7724.29086.545
80.6333.51990.064
90.4822.67792.741
100.3852.14194.882
110.3121.73196.613
120.1690.94197.554
130.1300.72098.274
140.1040.58098.854
150.0900.49899.351
160.0620.34499.696
170.0470.25999.954
180.0080.046100.000
Extraction Method: Principal Axis Factoring.
Table 5. Rotated factor matrix of the effects of adopting technological innovations in the air cargo logistics industry.
Table 5. Rotated factor matrix of the effects of adopting technological innovations in the air cargo logistics industry.
Factor
123
Efficient Operations0.780
Integrated Operations0.732
Simplified Handling Process0.728
Monitor Demand Variability0.704
Technology Diffusion0.667
Forecasts Uncertainty0.614
Improved Warehousing Operations 0.767
Equipment Utilisation 0.704
Inventory Accuracy 0.616
Improved Operations 0.601
Reduced Transport Cost 0.565
Reduced Clearing Time 0.550
Reduced Delivery Time 0.769
Order Accuracy 0.669
Delivery within Time 0.657
Contribute to Cargo Supply 0.650
Factor NameEfficient OperationsImproved WarehousingImproved Service Delivery
Extraction Method: Principal Axis Factoring. Rotation Method: Varimax with Kaiser Normalization.
Table 6. Model summary of the effect of technological innovations in the air cargo logistics industry.
Table 6. Model summary of the effect of technological innovations in the air cargo logistics industry.
ModelRR SquareAdjusted R SquareStd. Error of the Estimate
10.604 a0.3650.3610.86118
a. Predictors: (Constant), EffOpe, ImpWar.
Table 7. ANOVA a of the effects of technological innovations in the air cargo logistics industry.
Table 7. ANOVA a of the effects of technological innovations in the air cargo logistics industry.
ModelSum of SquaresDfMean SquareFSig.
1Regression157.632278.816106.2730.000 b
Residual274.4063700.742
Total432.038372
a. Dependent Variable: SerDel. b. Predictors: (Constant), EffOpe, ImpWar.
Table 8. Coefficients a of adopting technological innovations for air cargo quality service delivery.
Table 8. Coefficients a of adopting technological innovations for air cargo quality service delivery.
ModelUnstandardised CoefficientsStandardised CoefficientsTSig.Collinearity Statistics
BStd. ErrorBetaToleranceVIF
1(Constant)0.8420.203 4.1380.000
ImpWar0.4980.0590.4468.3970.0000.6081.644
EffOpe0.2440.0600.2154.0410.0000.6081.644
a. Dependent Variable: SerDel.
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Adenigbo, A.J.; Mageto, J.; Luke, R. Adopting Technological Innovations in the Air Cargo Logistics Industry in South Africa. Logistics 2023, 7, 84. https://doi.org/10.3390/logistics7040084

AMA Style

Adenigbo AJ, Mageto J, Luke R. Adopting Technological Innovations in the Air Cargo Logistics Industry in South Africa. Logistics. 2023; 7(4):84. https://doi.org/10.3390/logistics7040084

Chicago/Turabian Style

Adenigbo, Adedotun Joseph, Joash Mageto, and Rose Luke. 2023. "Adopting Technological Innovations in the Air Cargo Logistics Industry in South Africa" Logistics 7, no. 4: 84. https://doi.org/10.3390/logistics7040084

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