4. Applied Research Design and Evidence

Introduction: why research design matters in logistics

Logistics and supply chain decisions are increasingly expected to rest on evidence rather than intuition. Decisions about warehouse location, inventory policy, transport procurement, supplier resilience, delivery performance, sustainability, and digital transformation affect cost, service, risk, and stakeholder relationships. Yet operational data alone does not automatically constitute useful research. Data must be connected to a clearly defined problem, a defensible method, appropriate analysis, and conclusions that are proportionate to the evidence.

Applied research in logistics is concerned with understanding and improving real organisational situations. It may investigate why order fulfilment is unreliable, how a new warehouse management system affects productivity, whether supplier collaboration improves resilience, or how customers evaluate last-mile delivery. The researcher must move from a practical concern to a researchable question, examine what is already known, collect suitable evidence, and evaluate the credibility and limitations of the findings.

This chapter presents the design logic required for a postgraduate applied logistics project. It examines research paradigms, question formulation, literature and industry evidence, case study and field methods, sampling, validity, reliability, mixed methods, ethics, and the use of digital data. The central principle is alignment: the research question, evidence, method, analysis, and conclusion must support one another.

Learning objectives

By the end of this chapter, students should be able to:

1. Distinguish between a management problem, a research problem, a research aim, objectives, and research questions.

2. Develop focused and answerable research questions for logistics and supply chain studies.

3. Critically review academic literature and industry evidence.

4. Select and justify case study, survey, interview, observation, or mixed-method designs.

5. Apply appropriate principles of sampling, validity, reliability, and research quality.

6. Recognise ethical, governance, confidentiality, and data-protection requirements.

7. Design an evidence-based applied logistics project and identify its limitations.

8. Evaluate how digital systems, operational data, and analytics can strengthen or distort research conclusions.

From an operational concern to a researchable problem

A management problem describes an undesirable condition or decision requirement. For example, “warehouse picking costs are rising” is a management problem. A research problem explains what is not sufficiently understood about that condition. It might be: “The organisation does not know whether rising picking costs arise primarily from layout design, workforce scheduling, order-profile changes, or system practices.” The distinction is important because a project that merely recommends a solution may lack analytical depth, whereas research investigates relationships, causes, experiences, or alternatives systematically.

A research aim states the overall purpose of the study. A useful aim is neither so broad that it becomes unmanageable nor so narrow that it becomes trivial. An example is: “To evaluate how warehouse layout and workforce scheduling influence picking productivity in a regional distribution centre.” Research objectives divide the aim into achievable tasks, such as reviewing relevant theory, measuring productivity patterns, exploring employee experiences, and evaluating improvement options.

Research questions express what the study will answer. They should identify the phenomenon, context, population or unit of analysis, and, where relevant, the relationship being examined. “How do layout and scheduling influence picking productivity in the distribution centre?” is more useful than “What are the challenges of warehousing?” A question should be feasible within the available time, access, skills, and data.

The question may be descriptive, explanatory, evaluative, exploratory, or predictive. Descriptive questions ask what is happening; explanatory questions ask why or how; evaluative questions assess the effects or value of an intervention; exploratory questions investigate an under-researched issue; and predictive questions estimate likely outcomes. Many applied logistics studies combine these purposes, but the dominant purpose should be clear.

A strong question is specific without being artificially restrictive. It avoids assumptions such as “Why has the successful automation programme improved performance?” before success has been established. It also avoids questions that can be answered only through opinion when objective evidence is required. A practical test is whether the researcher can identify the data needed, the people or records from whom it will be obtained, and the analysis that will connect evidence to the question.

Conceptual foundations: paradigms and research logic

A research paradigm consists of assumptions about reality, knowledge, and how knowledge can be developed. Positivist and post-positivist approaches generally emphasise measurable variables, systematic observation, and the testing of relationships. They are often suitable for surveys, operational performance data, experiments, and statistical analysis. A study examining whether delivery-time variability is associated with customer satisfaction may adopt this orientation.

Interpretivist approaches emphasise how people understand and experience organisational reality. They are appropriate when the researcher needs to understand why planners resist a new system, how suppliers interpret collaboration, or how drivers experience route-control policies. Interviews, observation, and qualitative case studies are common methods.

Pragmatism focuses on the research problem and permits the use of methods that best answer it. A pragmatic researcher may combine transaction data with interviews because numerical evidence identifies the pattern while qualitative evidence explains it. Pragmatism does not mean that methods can be combined casually; the relationship between different forms of evidence must be designed and explained.

Deductive reasoning begins with concepts or propositions derived from existing theory and examines them using evidence. Inductive reasoning develops concepts or explanations from observed data. Abductive reasoning moves iteratively between evidence and theory, asking which explanation best accounts for the findings. Applied supply chain projects often use an abductive logic because researchers encounter unexpected operational conditions and refine their interpretation during fieldwork.

A conceptual framework is a structured representation of the concepts expected to matter in the study. For example, a framework for supplier resilience may connect information sharing and supplier visibility to disruption response, with governance quality and relationship trust as contextual influences. The framework is not proof of causation; it guides data collection and analysis and makes the researcher’s assumptions visible.

Building the evidence base

A literature review is an analytical account of knowledge relevant to the research problem. It is not a sequence of summaries. A strong review compares definitions, theories, methods, findings, and limitations. It identifies agreement, disagreement, gaps, and the contribution that the proposed study can make.

The review should begin with search concepts rather than a single phrase. For a project on last-mile delivery, search terms might include delivery reliability, failed delivery, customer satisfaction, route optimisation, urban logistics, and service quality. Academic databases, institutional repositories, professional bodies, government publications, and reputable industry reports may all be relevant, but they serve different purposes.

Peer-reviewed academic research is valuable for theory and methodological scrutiny. Industry evidence can reveal current practices, benchmarks, regulatory changes, and operational priorities. Company reports and consultancy publications may provide useful contextual information but can contain commercial bias, selective reporting, or unclear methods. Researchers should evaluate authority, purpose, currency, method transparency, sample coverage, and consistency with other sources.

A literature matrix helps organise the review. Typical fields include author and year, context, research question, theoretical lens, method, sample, principal findings, limitations, and relevance to the project. The matrix supports synthesis by making patterns visible. It also reduces the risk of citing sources that are only superficially related to the topic.

The review should lead to a gap, but a gap need not mean that nobody has studied the subject. It may be a contextual gap, where a theory has not been examined in a particular logistics setting; a methodological gap, where existing studies rely on one type of evidence; a practical gap, where managers lack guidance; or a contradictory evidence gap, where findings differ. The project’s contribution should be realistic. A postgraduate applied project may provide a carefully bounded diagnosis or evaluation rather than a universal theory.

Choosing a research design

Research design is the plan linking questions to evidence and analysis. The choice should follow the question, not personal preference or the perceived prestige of a method.

A case study investigates a contemporary phenomenon in its real-life context, especially when the boundary between phenomenon and context is not clear. It is well suited to distribution centres, transport networks, procurement programmes, digital implementations, or disruption events. A single case allows depth; multiple cases support comparison but require greater access and analytical discipline. The case must be bounded by location, organisation, process, time period, or event.

Case evidence may include interviews, observations, documents, performance records, system extracts, and photographs where permitted. Using several sources creates opportunities for triangulation. However, a case study is not automatically rigorous merely because it contains many interviews. The researcher must explain case selection, evidence collection, coding, and the basis for conclusions.

Surveys are useful for measuring perceptions, practices, attitudes, or reported behaviours across a defined population. A questionnaire on carrier collaboration might measure information sharing, trust, joint planning, and perceived service performance. Survey questions must be unambiguous, use appropriate response scales, and avoid double-barrelled statements such as “The system is fast and reliable.” Pilot testing can identify confusing wording and excessive completion time.

Interviews are suitable for accessing experience, reasoning, interpretation, and sensitive organisational knowledge. Structured interviews use standardised questions; semi-structured interviews combine a common guide with probing; unstructured interviews are more open-ended. Semi-structured interviews are particularly useful in applied logistics because they allow comparable coverage while preserving operational detail. The interviewer should avoid leading questions and distinguish what participants say from what the researcher infers.

Observation captures work as performed rather than work as described. In a warehouse, observation may reveal travel interruptions, informal workarounds, congestion, or differences between standard operating procedures and actual practice. Observation requires a clear focus, a recording protocol, and attention to the researcher’s presence. Covert observation is ethically problematic in most organisational projects, particularly where employees may be identifiable.

Document and secondary-data analysis can be highly effective. Sources include transport management system records, warehouse scans, purchase orders, claims, customer complaints, audit reports, and sustainability data. Researchers must understand how the data were originally generated. A “late delivery” field may reflect a carrier scan rather than the customer’s actual receipt time. Missing values, changed definitions, duplicated transactions, and system migrations can affect conclusions.

Mixed methods combine quantitative and qualitative evidence in a planned design. In an explanatory sequential design, performance data are analysed first and interviews then explain unexpected patterns. In an exploratory sequential design, interviews help develop concepts or survey items, followed by a broader survey. In a convergent design, both forms of data are collected during a similar period and integrated during interpretation. Integration is essential: simply placing statistical results beside quotations does not create a mixed-method study.

Sampling and access to evidence

Sampling determines which units, people, events, or records are included. The population is the full set relevant to the question; the sample is the subset studied. Probability sampling gives units a known, usually non-zero chance of selection and supports statistical generalisation when properly implemented. Simple random, stratified, systematic, and cluster sampling are common forms.

Non-probability sampling is often more realistic in organisational projects. Purposive sampling selects participants because they possess relevant knowledge, such as transport planners or procurement managers. Convenience sampling uses those easiest to access but is vulnerable to bias. Snowball sampling can help reach specialist or difficult-to-identify participants, although initial contacts may shape the sample excessively. A researcher should justify the choice and state what kinds of generalisation are not possible.

Sample size depends on purpose, population variability, design, precision requirements, and resources. Statistical sample-size calculations may be appropriate for surveys, but a larger sample cannot correct a biased sampling frame or poor measurement. In qualitative work, adequacy is judged through information power or saturation: collection may stop when additional evidence adds little meaningful insight to the research question. These are judgements that must be documented rather than used as vague claims.

Access is both a practical and methodological issue. A project relying only on senior managers may exclude warehouse employees, drivers, customers, or suppliers whose experiences differ. Gatekeepers can facilitate access but may also influence who participates and what is disclosed. Researchers should seek multiple perspectives where the question concerns a process involving different actors.

Establishing research quality

Validity concerns whether a study measures or explains what it claims to measure or explain. Construct validity is strengthened when concepts are clearly defined and measured through suitable indicators. Internal validity concerns the credibility of causal or explanatory claims within the study. External validity concerns the extent to which findings can transfer or generalise beyond the setting. In qualitative research, transferability depends on detailed contextual description and the reader’s judgement about similarity.

Reliability concerns consistency. A reliable survey instrument produces reasonably stable results under comparable conditions, although reliability alone does not establish validity. Internal consistency may be assessed for multi-item scales, while test-retest reliability examines stability over time. In qualitative research, dependability is supported through a transparent research record, consistent procedures, coding explanations, and reflexive consideration of changes during the project.

Researchers can strengthen quality through triangulation, pilot testing, negative-case analysis, member reflection, peer review, an audit trail, and clear operational definitions. Triangulation does not mean forcing all sources to agree. Contradictions may reveal differences between formal performance and lived experience, or between managerial assumptions and operational reality.

Measurement deserves particular attention in logistics. “On-time delivery,” “inventory accuracy,” “supplier reliability,” and “carbon performance” can each be defined in several ways. Before collecting data, the researcher should specify the unit of analysis, time window, numerator and denominator where relevant, exclusions, and treatment of missing data. Without such definitions, apparently precise metrics may produce misleading comparisons.

Causal claims require caution. If a warehouse introduces automation and productivity rises, the change may also reflect seasonal demand, workforce learning, altered order profiles, or staffing changes. A before-and-after comparison is informative but does not by itself prove that automation caused the improvement. Where feasible, comparison sites, interrupted time series, control variables, or process-tracing evidence can strengthen causal reasoning.

Ethics, governance, and professional responsibility

Research ethics protects participants, organisations, communities, and the integrity of knowledge. Participants should receive understandable information about the purpose, procedures, risks, use of data, voluntary nature of participation, and withdrawal arrangements. Consent should be informed and appropriate to the context. A manager’s request that employees participate does not automatically constitute free consent, particularly where employment power relationships exist.

Confidentiality means limiting access to identifiable information; anonymity means that identity cannot reasonably be linked to the data. In small logistics sites, complete anonymity may be impossible because job roles and incidents are distinctive. Researchers should avoid unnecessary identifiers, aggregate quotations where appropriate, secure files, control access, and report findings without exposing individuals to retaliation or commercial harm.

Data governance includes lawful collection, retention, storage, transfer, and deletion. Personal data, employee monitoring records, customer addresses, and supplier information require particular care and compliance with applicable institutional and legal requirements. Operational data should not be repurposed casually. A dataset collected for payroll or security may not be ethically suitable for research without additional approval and safeguards.

Researchers must manage conflicts of interest and sponsorship. An organisation funding a project may reasonably request confidentiality, but it should not control the analysis or suppress inconvenient findings. Fabrication, falsification, plagiarism, selective reporting, and misrepresentation of sources are serious breaches of research integrity. A professional report should distinguish evidence, interpretation, assumption, and recommendation.

Ethical review should occur before fieldwork, not after data have been collected. Risk assessment should consider physical safety in warehouses and transport environments, psychological discomfort, employment consequences, commercial sensitivity, and the potential misuse of analytics. Researchers must follow site safety rules and should never interrupt hazardous operations for data collection.

Digital evidence, analytics, and emerging practice

Digital logistics systems create opportunities for high-resolution research. Warehouse management systems, telematics, RFID, Internet of Things devices, electronic proof of delivery, and procurement platforms can provide timestamps and process traces. Process mining can help reconstruct the actual sequence of order, picking, packing, dispatch, and delivery events and identify bottlenecks or deviations.

However, digital traces are not neutral observations. They reflect system design, scan discipline, master-data quality, incentives, and organisational routines. Artificial intelligence and predictive analytics may identify patterns in demand, delays, or supplier risk, but model outputs depend on training data and assumptions. Researchers should examine data provenance, missingness, algorithmic bias, explainability, and the consequences of acting on predictions.

A sound applied project uses analytics to support, not replace, research judgement. A dashboard may show that one shift has lower productivity, but interviews and observation may reveal differences in product mix, equipment availability, or reporting practices. Responsible research therefore combines technical analysis with contextual interpretation and communicates uncertainty clearly.

Illustrative case: investigating failed deliveries in an urban network

Background

The following is a fictional illustrative case. MetroLink Distribution, a regional parcel operator, reported that failed first-attempt deliveries had increased over a six-month period. Management considered purchasing route-optimisation software, but the operations director wanted evidence that identified the principal causes before approving the investment.

Situation and research problem

The initial management question was, “How can failed deliveries be reduced?” The research problem was narrower: the organisation lacked a reliable explanation of whether failures were mainly caused by recipient absence, inaccurate address information, driver route design, promised delivery windows, or communication practices.

The study aim was to evaluate the operational and customer-related factors associated with first-attempt delivery failure in one urban depot. The research questions were:

1. What patterns characterise failed first-attempt deliveries during the study period?

2. How do drivers and customer-service staff explain the principal causes?

3. Which improvement options are feasible within the depot’s current operating model?

Design and evidence

A mixed-method design was selected. First, the researcher analysed six months of delivery records, including postcode sector, attempted time, delivery window, failure code, vehicle route, and subsequent outcome. Records with missing or inconsistent timestamps were identified rather than silently excluded. Second, purposive interviews were conducted with drivers, dispatch planners, and customer-service employees. Third, the researcher observed dispatch preparation and a sample of route briefings.

The quantitative analysis described failure rates by time window, route cluster, and failure code. It did not assume that failure codes were accurate; interview and observation evidence were used to assess their meaning. Interview transcripts were coded into themes such as address quality, recipient availability, route pressure, communication, and system usability. Findings were integrated by comparing statistical patterns with operational explanations.

Analysis and findings

The records showed that failures were concentrated in particular delivery windows and route clusters rather than being evenly distributed. However, the largest recorded category, “recipient unavailable,” was found to include several different situations. Drivers used it when access instructions were unclear, when a building reception desk was closed, and when route delays made the original time window unrealistic.

Interviews indicated that planners sometimes assigned routes using average travel times that did not reflect peak congestion. Drivers also reported that address notes were difficult to locate in the handheld system. Customer-service staff described repeated address corrections that were not consistently transferred to the master record. Observation showed that dispatch briefings focused on parcel volume but rarely reviewed access risks or high-failure addresses.

Approach and proposed solution

The evidence did not support an immediate conclusion that new optimisation software alone would solve the problem. The proposed intervention had four elements: improve address-data governance; create a standard process for recording building access information; review delivery windows using route-level travel evidence; and pilot proactive customer messages for high-risk stops. A limited route-optimisation trial was recommended only after data definitions and address quality had improved.

The evaluation plan specified first-attempt failure rate as the primary outcome, with customer complaints, driver workload, and cost per successful delivery as secondary outcomes. A comparable route group and a defined pre- and post-intervention period were recommended to improve interpretation. The study also identified that a lower failure rate would not be a sufficient success measure if drivers experienced unsafe time pressure or customers received less reliable information.

Outcome and learning points

In this illustrative case, the value of research lay in separating a broad operational symptom from multiple underlying mechanisms. The project demonstrated the importance of defining measures, checking the meaning of system codes, combining records with practitioner knowledge, and designing recommendations that could be evaluated. It also showed why technology procurement should follow diagnosis rather than substitute for it.

Common weaknesses and how to avoid them

Several errors recur in applied logistics projects. The first is a topic that is too broad, such as “the future of supply chain management.” Narrowing the unit, context, decision, and period makes research feasible. The second is a method chosen because it is familiar rather than appropriate. A survey cannot adequately explain complex workarounds if participants have no opportunity to elaborate; interviews alone cannot estimate the prevalence of a problem across a large workforce.

Other weaknesses include treating company data as objective without examining its creation, relying on a single managerial perspective, confusing correlation with causation, using poorly defined performance measures, and reporting recommendations that exceed the evidence. Researchers also sometimes promise anonymity that cannot realistically be maintained in a small site, or collect more data than they can analyse responsibly.

A practical quality check asks: Does every research question have a corresponding source of evidence and analysis? Is the sample justified? Are alternative explanations considered? Are limitations visible? Could another competent researcher understand how the conclusion was reached? These questions are useful at proposal, fieldwork, analysis, and reporting stages.

Best-practice design sequence

A defensible applied project normally proceeds through a connected sequence:

• define the decision or operational problem;
• formulate an aim, objectives, and focused questions;
• review theory, prior studies, and relevant industry evidence;
• develop a conceptual framework and select a research design;
• identify the population, case boundary, sample, measures, and access arrangements;
• obtain ethical and organisational approval;
• pilot instruments and procedures;
• collect and secure evidence systematically;
• analyse data in a manner suited to the questions;
• integrate evidence, examine rival explanations, and acknowledge limitations;
• produce recommendations linked to findings and an evaluation plan.

This sequence is not mechanically linear. Literature review may refine the questions, pilot work may expose measurement problems, and early analysis may require clarification of concepts. Nevertheless, changes should be documented so that the final report remains transparent.

Chapter Summary

Applied logistics research converts an operational concern into a structured investigation. The process begins by distinguishing the management problem from the research problem and by developing an aligned aim, objectives, and questions. Literature and industry evidence should be critically synthesised rather than merely described. Research design must follow the purpose of the study: case studies provide contextual depth, surveys measure patterns across populations, interviews reveal experience and reasoning, observation captures work as performed, and mixed methods integrate complementary forms of evidence.

Sampling, measurement, validity, reliability, triangulation, and transparent analysis determine the credibility of findings. Ethical practice requires informed participation, confidentiality, secure data governance, protection from harm, and research integrity. Digital systems and analytics can provide valuable process evidence, but their outputs must be examined in relation to data quality, system design, bias, and organisational context. The strongest applied projects make bounded claims, recognise limitations, and connect recommendations to an explicit evaluation plan.

Key Terms

Research problem: A focused statement of what is insufficiently understood or needs systematic investigation.

Research aim: The overall purpose of a research project.

Research objective: A specific, achievable task contributing to the research aim.

Research question: An answerable question that directs evidence collection and analysis.

Research paradigm: Assumptions about reality, knowledge, and appropriate ways of investigating it.

Conceptual framework: A structured representation of concepts and relationships guiding a study.

Deduction: Reasoning from theory or propositions toward empirical testing.

Induction: Developing concepts or explanations from observed evidence.

Abduction: Iterative reasoning between evidence and possible explanations.

Case study: An in-depth investigation of a bounded phenomenon in its real context.

Survey: A structured method for collecting comparable information from a defined population or sample.

Interview: A purposeful conversation used to obtain participants’ accounts, experiences, or interpretations.

Observation: Systematic recording of activities, behaviours, or processes as they occur.

Mixed methods: A planned integration of quantitative and qualitative approaches.

Population: The complete set of units relevant to a research question.

Sample: The subset of a population included in a study.

Validity: The extent to which evidence supports the intended interpretation or measurement.

Reliability: The consistency of a measurement or research procedure.

Triangulation: Comparing multiple sources, methods, researchers, or perspectives to strengthen interpretation.

Research integrity: Honest, transparent, and responsible conduct of research.

Review Questions

Short Answer Questions

1. Distinguish between a management problem and a research problem in a logistics context.

2. What is the purpose of a conceptual framework?

3. Define purposive sampling and identify one situation in which it is appropriate.

4. Why is reliability insufficient on its own to establish research quality?

5. State two ethical risks associated with using employee or customer logistics data.

Long Answer Questions

1. Explain how a broad logistics topic can be converted into an aligned aim, objectives, and research questions.

2. Compare case study, survey, interview, and observation designs, including the types of questions each can answer well.

3. Discuss how a literature review should evaluate academic and industry sources rather than merely summarise them.

4. Explain the relationship among validity, reliability, triangulation, and the credibility of applied research.

5. Discuss the ethical and governance issues that arise when digital logistics records are used for research.

Analytical / Application Questions

1. A distribution centre reports increasing picking errors after introducing a new warehouse management system. Design a mixed-method study, specifying the questions, evidence sources, sample, and approach to analysis.

2. A survey finds that suppliers who report high trust also report better delivery performance. Identify at least three alternative explanations and propose a design that would strengthen causal interpretation.

3. A transport company provides researchers with driver-monitoring data but asks them not to interview drivers. Critically assess the limitations of this evidence and propose an ethically stronger research design.

22. Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes a research problem from a management problem?

A. A research problem states only the financial cost of an issue.

B. A research problem identifies what is insufficiently understood and requires systematic investigation.

C. A research problem is always expressed as a statistical hypothesis.

D. A research problem is the same as a recommended solution.

Correct Answer: B. A research problem identifies what is insufficiently understood and requires systematic investigation.

Question 2

A researcher first analyses delivery records and then interviews planners to explain unexpected patterns. Which mixed-method design is being used?

A. Exploratory sequential design

B. Convergent design

C. Explanatory sequential design

D. Cross-sectional sampling design

Correct Answer: C. Explanatory sequential design

Question 3

Which practice most directly strengthens construct validity in a logistics study?

A. Increasing the number of interviewers without training

B. Defining concepts clearly and using indicators appropriate to those concepts

C. Removing all contradictory observations

D. Selecting only the most senior participants

Correct Answer: B. Defining concepts clearly and using indicators appropriate to those concepts

Question 4

A researcher selects warehouse supervisors because they possess detailed knowledge of a picking process. This is an example of:

A. Simple random sampling

B. Cluster sampling

C. Purposive sampling

D. Systematic sampling

Correct Answer: C. Purposive sampling

Question 5

Why should researchers examine the meaning of a system field such as “late delivery” before analysing it?

A. System fields are always legally protected from analysis.

B. The field may reflect a particular scanning practice rather than the operational event assumed by the researcher.

C. Digital records cannot be used in applied research.

D. Statistical analysis automatically corrects data definitions.

Correct Answer: B. The field may reflect a particular scanning practice rather than the operational event assumed by the researcher.

Question 6

Which claim is most defensible after observing productivity rise following warehouse automation, without a comparison group?

A. Automation definitely caused the entire improvement.

B. The improvement proves the technology is cost-effective in all warehouses.

C. Productivity increased after implementation, but other explanations cannot be excluded.

D. No conclusion can be drawn from the data at all.

Correct Answer: C. Productivity increased after implementation, but other explanations cannot be excluded.

Question 7

What is the principal purpose of triangulation?

A. To ensure every evidence source produces identical findings

B. To compare different sources or methods and strengthen or refine interpretation

C. To replace ethical approval with multiple data sources

D. To increase the sample size of a qualitative study automatically

Correct Answer: B. To compare different sources or methods and strengthen or refine interpretation

Question 8

Which ethical situation most clearly creates a risk to voluntary consent?

A. An employee receives an information sheet before choosing whether to participate.

B. A manager instructs subordinates that participation is expected as part of their employment.

C. A researcher offers participants the opportunity to withdraw.

D. A study stores coded data in a restricted-access folder.

Correct Answer: B. A manager instructs subordinates that participation is expected as part of their employment.

Question 9

A survey statement reads, “The transport system is fast and reliable.” What is its main design weakness?

A. It uses a response scale.

B. It is double-barrelled because it measures two potentially different attributes.

C. It is too specific for a logistics study.

D. It cannot be administered online.

Correct Answer: B. It is double-barrelled because it measures two potentially different attributes.

Question 10

In the illustrative failed-delivery case, why was immediate purchase of route-optimisation software not recommended as the sole solution?

A. Technology is never relevant to delivery performance.

B. The evidence suggested that data quality, access information, time windows, and operational practices also contributed to failure.

C. Drivers refused to use any digital system.

D. The organisation had no delivery records.

Correct Answer: B. The evidence suggested that data quality, access information, time windows, and operational practices also contributed to failure.