Project title *
The Impact of Industry 5.0 to Achieve Net-Zero in Warehousing Through Innovative
Sustainable Technologies
What is the aim of your study? *
To study the effect of Industry 5.0 (I5.0) technologies on the warehouse environmental
sustainability performance indicators (ESPIs) to achieve net-zero emission rate in a circular
supply chain and to propose a conceptual implementation framework.
What are the objectives for your study? *
1. Conduct a systematic literature review on the following fields of study:
a. Industrial revolutions, Industry 5.0, and Industry 5.0 technologies
b. Smart Warehousing, Sustainable Warehousing, and Warehouse ESPIs
c. Sustainable Supply Chain (SSC), Circular Supply Chain (CSC), and Net-Zero
Supply Chain (NZSC)
2. Develop a theoretical framework to investigate the relationships between I5.0 and supply
chain (SC) decarbonization within the context of warehousing operations
3. Statistically analyse and test out the theoretical framework
4. Develop a conceptual implementation framework for I5.0 to improve warehouse
performance andachieve net-zero.
Are there any research partners (NOT including your supervisor) within the University
of Derby involved in the project?
N/A
Are there any research partners external to the University of Derby involved in the
project? *
Yes
No
If yes, please provide details
Screening
Does this project involve human participants? *
Yes
No
If yes, should your research adhere to the British Psychology Society (BPS) code of ethics and
conduct? *
Typically this relates to applications in the field of Psychology (if in doubt, please seek advice from a relevant
subject specialist)
i
Yes
No
Does your study involve data collection with any persons who could be considered vulnerable (under
18 years or the elderly, or those with physical or mental disabilities)? *
Yes
No
Does your project involve collecting data within NHS organisations or from any NHS employees or
patients? *
If yes, further approval will also be required (see guidance)
i
Yes
No
Does it involve collecting or analysing primary or unpublished data about people who have died, other
than data that is already in the public domain? *
Yes
No
Does your study involve direct access to an external organisation? *
If yes, an approval letter will need to be attached at the end of the application
i
Yes
No
Does your study involve species not covered by the Animals Scientific Procedures Act (1993)? *
i
Yes
No
Does your study involve ionising radiation? *
Yes
No
Does your study involve the evaluation of medical devices, or the testing of medicinal and
pharmaceutical products? *
i
Yes
No
Does your study involve Her Majesty's Prison and Probation Service? *
If yes, upload of approval will be required at the end of the application
Yes
No
Does your study involve serving offenders, professionals who work with them, or questions relating to
criminal offences? *
i
Yes
No
Does your study involve a need to see, acquire or store material that could be viewed as illegal or that
may attract the interest of the police, security or intelligence services? *
i
Yes
No
Will your study have any impact on the natural or built environment? *
This will apply to research focused on natural sciences and environmental projects
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Yes
No
Funding and previous applications
Has this research been funded by an external organisation (e.g. a research council or public sector
body)? *
Yes
No
If yes, please provide the name of funder:
Has this research been funded internally? *
Yes
No
If yes, please provide details
Name of internal fund *
University of Derby
Funding amount *
15609
Term of funding *
3 years
Date funding agreed *
21 Jan 2022
Have you submitted previous requests for ethical approval to the Committee that relate to this
research project? *
Yes
No
If yes, please provide previous application reference:
Study
Brief review of relevant literature and rationale for study *
Literature defines a warehouse as a place to receive, store, and ship materials. The circular supply
chain (CSC) (Farooque et al., 2019) requires more than a traditional warehouse, as current linear
warehouses are limited, not effective nor sustainable. Even a smart warehouse that relies on
automation and digitalization to complete common tasks and operations originally completed by
humans does not meet the needs of the CSC. And a sustainable warehouse that balances and
manages the economic, environmental, and social inputs and outputs of common tasks and
operations, is also not sufficient. The CSC requires an innovative warehouse rather than a clear-cut
definition one. It requires more than a smart sustainable place; it needs a new industrial
revolutionary warehouse or maybe a deindustrial one.
Industry 5.0 supports a vision of an industry that is looking beyond efficiency and productivity as the
only targets and strengthens the responsibility and the contribution of industry to society and the
environment. And as Industry 5.0 is still developing and evolving there are different definitions
explaining it (Maddikunta et al., 2022).
Michael Rada defines Industry 5.0 as a first industrial evolution led by the human based on the 6R
(Recognize, Reconsider, Realize, Reduce, Reuse and Recycle) principles of industrial upcycling, a
systematic waste prevention technique and logistics efficiency design to valuate life standard,
innovative creations and produce high-quality custom products (Rada, 2018).
And the European Economic and Social committee states that Industry 5.0, integrates the swerving
strengths of cyber-physical production systems (CPPS) and human intelligence to create synergetic
factories (Longo, Padovano and Umbrello, 2020).
The Industrial Revolution is the main cause of climate change and global warming, focused on
efficiency, effectiveness, and increasing productivity, and ignored the sustainable aspects of the
ecosystem. Moving forward from one stage of industrial maturity to another, reaching Industry 4.0,
the global annual temperature increased by 1.5 degrees Celsius.
If we want to keep moving forward, we need to change the methods of production or improve them
by focusing on the environmental and sustainable aspects and personalise production activities by
bringing back the human interaction closer to the design and decision-making processes. These are
the main principles Industry 5.0 is based on. So most likely what triggered the climate change
problem can also be used to treat it, after all.
This study aims to evaluate the warehouse environmental sustainability performance indicators and
how to decarbonize the SC ecosystem and reduce the CO2 and GHG emissions to net-zero through
enabling Industry 5.0 technologies. And to propose a conceptual implementation framework to
support the study (Andreadis, Garza-Reyes and Kumar, 2017).
I5.0 is an emerging idea that has huge potential to enable organisations to achieve net-zero in their
SC. However, the literature review carried out so far suggests that there are minimal research have
been accomplished on this topic. Research in this direction is necessary to explore opportunities
provided by I5.0 and how it could enable organisations to improve their warehouse performance
and decarbonize their supply chain.
Cited references for any sources in the sections on rationale, methods etc.
References:
• Andreadis, E., Garza-Reyes, J. and Kumar, V., 2017. Towards a conceptual framework for value
stream mapping (VSM) implementation: an investigation of managerial factors. International Journal
of Production Research, 55(23), pp.7073-7095.
• Farooque, M., Zhang, A., Thürer, M., Qu, T. and Huisingh, D., 2019. Circular supply chain
management: A definition and structured literature review. Journal of Cleaner Production, 228,
pp.882-900.
• F. Longo, A. Padovano, S. Umbrello, Value-oriented and ethical technology engineering in industry
5.0: a human-centric perspective for the design of the factory of the future, Applied Sciences 10 (12)
(2020) 4182.
• Maddikunta, P., Pham, Q., B, P., Deepa, N., Dev, K., Gadekallu, T., Ruby, R. and Liyanage, M.,
2022. Industry 5.0: A survey on enabling technologies and potential applications. Journal of
Industrial
Information Integration, 26, p.100257.
• Rada, M., 2018. INDUSTRY 5.0 definition. [online] Medium. Available at: <https://michaelrada.
medium.com/industry-5-0-definit
Outline of study design *
While you are designing a research study you must ask yourself: “What I am investigating?” and
“How I’ll reach this goal?”
Our research is investigating the relationship between innovation and sustainability inside a
warehouse environment to achieve a net-zero attainable, viable and -as possible- realistic strategy
for both the short-term and long-term visions in both developed and developing countries.
Based on the intensive and concentrated literature review for the past eight months, we found many
gaps and a lot of rooms to explore, such as: weak vs string sustainability, green vs sustainable supply
chain, smart vs sustainable warehouses, Industry 4.0 vs Industry 5.0 and beyond, Industry 5.0
sustainable oriented innovations, how to improve environmental performance in logistics generally
and in warehousing particularly, and how to enhance operational activities to achieve net-zero
strategy and many more grey areas in the field of supply chain management.
Based on the -dated yet if slightly modified workable- management theories we build a theoretical
framework and developed our hypotheses as follows:
H1 Supply chain sustainable innovations driven by the advanced technologies of Industry 5.0 have a
positive effect on warehouse activities.
H2 Warehouse activities positively impact warehouse's environmentally sustainable key
performance indicators.
H3 Warehouse's ESKPIs are positively correlated with Net-Zero emission rates.
H4. Industry 5.0 technologies have a positive impact on net- zero environmental performance.
Then we started designing the research onion. The first layer is the research philosophy, there are
four research philosophies: Positivism, Interpretivism, Realism, and Pragmatism. Generally, the most
common research philosophies applied in the field of supply chain and logistics management
research are positivism and interpretivism. These two philosophical perspectives have been used
significantly in supply chain management research due to the predominant nature of their approach.
According to positivism philosophy, social science research should follow the natural science
approach, which emphasises empirical observation, the discovery of causal laws, and value-free
inquiry. Investigating positivism in social science research entails comparing it to natural science,
where facts are derived from empirical study. Mainly, supply chain management research focus on
establishing reality by examining the causal relationship between variables. Because positivism can
establish facts of experience using a manner analogous to natural science, it has become the most
widely used philosophical position in supply chain research in this sense. The goal of the positivist
paradigm is to develop generalisations that resemble laws by establishing causal relationships
between the variables in research data.
Others claim that the social and business environments are too complex to be studied in the same
way as natural science. Agreeing to this viewpoint, interpretivism is probably how a research study
will be conducted. In this regard, interpretivism holds that since social science is complex and cannot
be explored through theory and scientific generalisation, it cannot be investigated like natural
science. Interpretivism is an epistemology that advocates that social world can only be understood
and interpreted through the perception of the researcher. This philosophy emphasises how research
examination should focus on people rather than objects. Since their interpretation of the research
phenomena is a key component of the study's findings, social actors are themselves included in the
investigation from a theoretical standpoint. As a result, researchers behave in accordance with their
roles and the interpretations they make of the social environment through their study. The resolve
of interpretivism is to generate new, rich, insightful, and significant interpretations of social scope
and considerations. Within supply chain management research, this refers to understanding
organisation from the perspective of various people, management and institutions and their wideranging opinion and reasoning towards organizational operations.
Both research philosophies: positivism and interpretivism are applied in this research.
The second layer is the research approach. Deductive and inductive are the two terms that the
second layer of the research onion includes. Our research follows a deductive methodology. The first
step in deductive research is developing a theoretical framework, which then results in the creation
of hypotheses that are tested empirically. Therefore, the research approach can be said to as
deductive if the research starts with the formation of a theory, which is frequently produced through
a thorough literature review, and the emphasis of the study is to construct a research plan to test
the hypothesis. The hypothesis is typically either confirmed or disproved using the deductive
method. As a result, the research proceeds logically from rules to theory to outcomes.
The third layer is the research strategy, which is the relationship between the research philosophy
and the data collecting and analysis method. When it comes to operations and business
management, there are seven important research strategies: experiment, survey, case study, action
research, grounded theory, ethnography, and archival research. Survey research is more closely
related to deductive reasoning, which incorporates the acceptance of empirical investigation of the
research hypothesis. The primary goal of the research is to establish a causal connection between
constructs, which is in line with explanatory research. Explanatory research seeks to establish cause and -effect relationship and determines to answer ‘what is the impact’ question, this makes
explanatory research more inclined with survey research. The outcome of explanatory research
might confirm or falsify the proposed hypotheses. Explanatory research is conducted through
surveys.
The fourth layer is time horizon. The determination of the timelines within the research is a very
important component. A researcher must determine if the data collected should be taken at a
specific time (cross sectional) or covers multiple times (longitudinal). This decision mainly depends
on the research questions. Cross sectional approach is considered for this research, as it involves
dealing with single point of time in the research, and is very often associated with survey strategy,
seeking to explain relationships between constructs.
Outline of study methods *
This layer of the research onion is known as the research choice. This layer helps you to know
whether it is fine to combine both quantitative and qualitative methodology or to use only one
methodology. Usually there are three research methods: mono-method, multi-method, and mixedmethod.
Quantitative and qualitative methods are the two major data collection strategies associated with
management research. The main differentiating aspect between qualitative and quantitative
methods is that qualitative research relies on non-numeric data (e.g., words, images, videos, and
clip), while quantitative technique relies on numeric data (numbers). Hence, survey-based research
is related to quantitative, and interview-based research is related with qualitative.
A researcher can adopt one or more data collection method. If the study is associated with only one
particular data collection technique, for example, questionnaire, it is referred to as a mono-method.
On the other hand, adopting multiple techniques in data collection is referred to as multi-method.
However, mixed-method is the process where the researcher combines both qualitative and
quantitative in the same study.
This research will adopt a mono-method quantitative technique and for that matter questionnaire
survey.
Our research will adopt questionnaire as the only strategy for data collection as within survey
method and among supply chain management research, questionnaires are the most common. A
questionnaire is described as a technique to collect data where each respondent is asked the same
set of questions. It is associated with explanatory research, helping in studying the cause and effect
relationship between variables.
As we are interested in studying the inter-relationship between Industry 5.0, warehouse activities,
environmental KPIs and net-zero strategy, adopting a questionnaire will be consistent with the aim
of the research. Nevertheless, many researchers have suggested that selecting the data collection
method is also dependent on resources such as: money, time, and personnel. A survey questionnaire
is relatively cost effective, efficient, and quicker to reach respondents.
To investigate and fully understand the various direct and indirect interrelationships between all
dependent and independent variables, we will develop a questionnaire to be answered by high-level
managers, supply chain and logistic specialists, and academics to investigate the strategies of
investing in Industry 5.0, strong sustainability development and net-zero supply chain, and the dayto-day warehouse operational activities and KPIs.
The questionnaire will be online internet-mediate self-administered as this approach is cheaper,
easy to administer, can reach wider respondents specially when the respondents are sparsely
located, automating the data is easy to undertake and capable to reach out to specific respondents
who have technical knowledge about the research.
The sample size required for structural equation modelling (SEM) analysis should range from 140400, this study is using a sample size of 400, which is the maximum requirement for SEM analysis.
The research will adopt multiple regression and structural equation modelling (SEM), which are the
major components of multivariate analysis. SEM is combination of factor analysis and multiple
regression that helps the researcher to simultaneously investigate a sequence of interconnected
dependence relationship between different variables employed in a study.
Based on the research objectives, questions and framework, the analysis type for this study is
determined. The multiple interrelationships between independent and dependent variables makes
SEM the appropriate method for analysis. Whereas SEM can estimate separate interdependent
multiple analysis at the same time in one study. Nevertheless, SEM can explore the relationship
between dependent and independent variables while determining the impact or effect of each
variable on another.
SEM can convert these relationships into a structural model that, for all dependent variables, is
analogous to a regression equation. Bias is reduced when data are analysed using SEM because all
measurements are made simultaneously. Additionally, SEM can find relationships in the structural
model that were previously missed by the researcher using modification indices, as well as new
prospective relationships.
Additionally, SEM can identify any new relationships that define the overall model and aids in the
development of new hypothese that the researcher could have missed.
One of the strongest uses of SEM is the inclusion of latent variables in the analysis. Latent variables
are variables that cannot be measured direct but through measurement items (observed variables).
SEM can measure latent variable by employing measuring items, which are collected through, for
example survey.
There are two approaches, where SEM can be performed: covariance based (CB-SEM) and partial
least square (PLS-SEM). Selecting the technique is usually based on the objective of the research, if
the objective of the research is to test theory, the appropriate technique to use is covariance based
(CB-SEM), but, if the objective of the research is to develop or build a new theory, the appropriate
technique to use is partial least square (PLS-SEM). CB-SEM is the most the appropriate analysis
technique for our study.
Please provide a detailed description of the study sample, covering recruitment, selection,
number, age and if appropriate, inclusion and exclusion criteria. *
Sampling, an important stage in conducting empirical research, involves choosing the ideal
individual, group, or events from which data is collected.
Due to time and financial constraints, it is almost impossible to gather data from almost every
feasible individual, or group regardless of the research questions and objectives
As a result, researchers use a variety of sampling approaches to restrict the amount of data they
need to collect to complete their study by considering a controllable subgroup within the greater
population that may be crucial to the research.
If the sample procedure was used correctly in this instance, it helps to generalise the results to
include the complete population that the subset represented.
There are five key steps in sampling process:
➢ Select the appropriate sampling technique.
➢ Identify the target population.
➢ Determine the appropriate sample size.
➢ Determining the sample frame
➢ Executing the sampling process
Sampling technique can be sorted into two types: probability sampling (representative sampling)
and non-probability sampling (judgemental sampling).
With probability sample, the possibility of each case being selected from the population is equal for
all cases. In this sense, each member of the population has equal chance of being selected by the
researcher to represent the total population. This process gives the researcher the opportunity to
statistically estimate the features of the population from the sample. Probability sampling is often
connected with survey research, quantitative research, and experimental strategy (Bryman and Bell,
2015).
Non-probability sample on the other hand, has no known or fixed probability of each case within the
population being selected. This makes it impossible for the researcher to answer research questions
or to fulfil research objectives that demand making statistical inferences about the unique features
of the population. Non-probability sampling is largely associated with qualitative research, where
the research is expected to collect a case that could provide in-depth information about research
phenomenon.
There are probability sampling techniques. Simple random sampling is a type of sampling where all
elements in the population are considered and each of them has the same chance of being selected
as a subject.
Simple random sampling will be used in this research, as our study does not apply face-to-face
interviews and do not use layered population. It is also relatively cost and time efficient.
Nevertheless, the research is in line with simple random sampling, because of the analysis type SEM-, as simple random sampling is highly linked with SEM because the estimation technique of
maximum likelihood associated with SEM requires that data generated must be done according to
simple random sampling method.
We are trying to examine the impact of Industry 5.0 technologies on the environmental KPIs in a
warehouse and generate a conceptual framework to be adapted globally to help reaching the netzero strategy goals, therefore, to gather the necessary information to answer the research question
and fulfil the research objectives, it is appropriate to appeal to different sectors and industries all
over the globe in both developed and developing countries. With respect to this, the target
population of this study is high-level and mid-level supply chain and logistics managers, sustainability
specialists, and technology innovation specialists. This target demographic is considered suitable
since it enables the researcher to connect with people who have competency and experience in
supply chain, sustainability and innovation and will be able to provide information consistent with
the data required for this study.
Based on the work of Hair et al. (2010) it is suggested that the required sample size when using
structural equation modelling (SEM) is between a range of 140-400. In their study, they
recommended using a minimum of five sample per observed variable when the researcher is using
SEM as an analysis method. Deriving from the developed theoretical framework we have 22
theoretical constructs with 73 observed variables. This indicates that the study requires a minimum
of 365 samples to be able to run SEM analysis. Based on the previous calculation, our research the
sample size will be of 400.
Finally, the sample frame is determined based on the sample size. The sample frame for any study is
the total list of all the elements within the population where the sample is drawn. This consists of
the number of the unit of the population whose opinion matters for the study.
According to Klassen and Jacobs (2001), the projected response rate for online survey in business
management research is within the range of 5-11%. Therefore, in order to achieve the sample size of
400, (sample size used in this study) a sample frame of 8000 is required. Thus (8000 ∗ (5/100) = 400).
Are payments or rewards/incentives (e.g. participant points) going to be made to the participants? *
Yes
No
Do you propose to carry out your project partly in a non-English language? *
Yes
No
If yes, please provide details
Based on this calculation the study sets a target of 8000 as a sample frame for this study.