Individual Report

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PERFORMING SECONDARY RESEARCH

MBA600 Week 8

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WEEK 8 FOCUSES ON TWO LEARNING OBJECTIVES

Undertake independent research to solve complex business problems.

Other learning objectives

Discuss and translate theory, skills and knowledge into effective management practice.

Acquire advanced knowledge and apply it in real workplace contexts to improve performance and competitive advantage.

Critically assess a diverse range of theories accumulated throughout the Masters’ qualification and the connections that exist between each one.

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QUICK REVIEW OF KEY

CONCEPTS What we learned in Week 7

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WHERE RESEARCH FITS IN

THE STRATEGY PLANNING PROCESS

Validating the decisions and assumptions made during the Strategy Planning Process

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THE BALANCED SCORECARD

FINANCIAL AND OPERATIONAL METRICS

Kaplan, R. S., & Norton, D. P. (1993). Putting the Balanced Scorecard to Work. Harvard Business Review.

WHAT WE LEARNED IN WEEK 7

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WHAT WE WILL LEARN THIS

WEEK

CONSIDERING STRATEGY PROBLEMS

Design thinking process

‘The essence of strategy is choosing what not to do.’ – Michael Porter, Harvard Business School

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WHAT WE WILL LEARN THIS

WEEK

RESEARCHING STRATEGY PROBLEMS

Creating positive customer and employee experiences are the reasons for secondary research

How strategy problems can be verified with statistical techniques

Support for your Assessment 2 due in Week 9, where the marking criteria includes

A summary of recommendations that identify areas of focus and opportunity to enhance the organisations performance in the future

We will focus on analysis planning to substantiate your recommendations

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WEEK 8 EXTENDED READINGS

Interaction Design Foundation

https://www.interaction-design.org

UX Collective

https://uxdesign.cc

Nielsen Norman Group

https://www.nngroup.com/articles

Forbes, A day in the life of customer service

https://www.forbes.com/sites/insights- pega/2018/01/03/a-day-in-the-life-in-customer- service/#66235df06e02

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QUINT- ESSENTIAL STRATEGY PROBLEM

Positive Customer Experience

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QUINTESSENTIAL STRATEGY PROBLEM

DESIGNING PRODUCTS AND SERVICES

POSITIVE CUSTOMER EXPERIENCE

It's important to ensure a positive customer experience so that customers:

build brand loyalty and affinity,

evangelize your product or service to their friends,

leave you positive customer reviews,

with the business objectives of retaining revenue and gaining new customers.

Source: HubSpot blog, 7 October 2019

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PRODUCT DESIGN

SHOP TILL YOU DROP

DESIGN THINKING PRODUCT/SERVICE DESIGN RESEARCH FOR COMPETITION STRATEGY

Equally applicable to all stages in the supply chain

A supply chain refers to the network of suppliers and distributors of a specific product/service to a final customer

The d.school at Stanford University is a prominent approach to user experience (UX) research

https://dschool.stanford.edu/

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DESIGN THINKING MODEL STANFORD UNIVERSITY

Ideate TestEmpathise Define Prototype

Research the customer/user

needs

Define the strategy problem you’re trying to solve

Cautiously consider the

validity of your assumptions

Research objective is to gain

insight, not data and

analytics

State the customer’s/user’s

needs and problems

Gather the data that you

identified in the Empathise

stage

Analyse data to define the

strategy problem statement,

as human-centred as

possible

Challenge assumptions and

create ideas

Interpret primary and

secondary data for meaning,

‘think outside the box’

Identify alternative and

innovative solutions to the

problem statement

Start to create solutions

Experiment to identify the

best possible solution

Produce inexpensive, scaled-

down versions of the

solution

Try out your solutions

Check for positive customer

experience and/or

technology adoption

May discover or refine other

problems to solve in the

future

Adopt a ‘growth mindset’,

learn to finish the project

and start another

Sources: Interaction Design Foundation, UX Collective

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DESIGN THINKING MODEL STANFORD UNIVERSITY

Ideate TestEmpathise Define Prototype

Researching

Primary and Secondary Research

Field study (ethnography)

Diary study (observation)

Structured and semi- structured Interviews

Focus groups

Conceptualising

Competitive analysis

Comparative (design) review

Persona building

Requirements analysis, user

stories, journey mapping

Appraising

Benchmarking

Accessibility evaluation

Design sprints (agile

projects)

Consulting

Sketch

Wireframes

Concept models (low fidelity

design)

Application mock-up (high

fidelity design)

Verifying and validating

Primary and Secondary

Research

Requirements testing

User/customer evaluation

Source: Interaction Design Foundation; Nielsen Norman Group

We will discuss primary research in Week 9

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WORKSHOP TIME

Is the management of customer experience a strategic capability?

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IS THE MANAGEMENT OF CUSTOMER EXPERIENCE A

STRATEGIC CAPABILITY?

WORKSHOP

In groups or individually, define/critique a customer experience strategy for your selected company (30 minutes)

Customer Experience Strategy techniques

‘Day in the life’ storyboard

Map out their entire schedule; essentially describing what they do when (assuming you know the customer/persona)

Customer journey map

Investigate the relationship between a customer and an organisation, brand, or product over time; considering all touchpoints and channels of interaction

Great example in 2018 at https://www.forbes.com/sites/insights-

pega/2018/01/03/a-day-in-the-life-in-customer-

service/#66235df06e02

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DEVELOPING A DATA ANALYSIS PLAN

Ensure your secondary research is comprehensive

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NECESSARY CONSIDERATIONS

CONTENTS OF A DATA ANALYSIS PLAN

Research questions and/or hypotheses

How can you test the strategy problem?

Dataset(s) for secondary data

What is the type of data and validity of the dataset

Criteria for including and excluding data

Quality control of ‘good’ and ‘bad’ data

Key variables for analysis

Independent, dependent and antecedent variables

Statistical methods and software

Measurement techniques

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TYPES OF DATA Contents of a Data Analysis Plan

Use existing (secondary) data first

Secondary research data is free or at low cost

Secondary data often based on actual company sales; or research publications with large sample sizes

Secondary data may not be updated regularly

Secondary data may leave out variables specific to a context or strategy problem

Collect new (primary) data when needed

Primary data is directly relevant to a research/strategy problem

Primary data is current data

Primary data is expensive

Interpretations of primary data may be ambiguous if not based on existing theory and knowledge (e.g. ‘fishing for explanations’)

Legend: Pros are green; Cons are red

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SOURCES OF DATASETS Contents of a Data Analysis Plan

Qualitative secondary datasets

Research reports

Thesis

Books

Focus group transcripts

Semi-structured and structured interviews

Observation notes

Field notes

Government publications

Blogs, whitepapers, social media

Quantitative secondary datasets

Australian Bureau of Statistics

Census

Electoral statistics

Health and welfare

Government publications

Australian Stock Exchange

Yahoo.com

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KEY QUANTITATIVE VARIABLES The objective is to gather non-numerical data for why and how a smaller sample of people feel about something

KEY QUANTITATIVE VARIABLES The objective is to gather statistics for explaining how much and generalizability to a larger sample DEFINING KEY

VARIABLES QUALITATIVE EXAMPLES QUANTITATIVE EXAMPLES What did you like about … How many people participated

How can it be improved How much did it cost

What difference did it make in your life

How long did it run for

Who would you recommend it to

How many times did you recommend it

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ANALYSIS OF QUALITATIVE

DATA

STATISTICAL METHODS CONTENTS OF A DATA ANALYSIS PLAN

Content analysis

Categorising verbal or behavioral data

Narrative analysis

Categorising stories and experiences of each research participant in the resource sample

Discourse analysis

Categorising the natural language discussions (verbal and written)

Framework analysis

Coding, mapping and charting non-numerical data for quantitative analysis

Grounded theory

Evaluating a single case to develop a new theory or contribute to existing theory

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ANALYSIS OF QUALITATIVE

DATA

STATISTICAL METHODS CONTENTS OF A DATA ANALYSIS PLAN

Content analysis provides a way to investigate what people say, see, hear, and write to understand underlying biases and intentions.

Analyzing birthday cards to understand age norms

Reading men’s fitness magazines to discover patterns in their portrayals of men

Recording conversations between physicians and patients to discern the relations of power between the two parties

Watching various television shows from different eras to determine how minorities are depicted

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ANALYSIS OF QUALITATIVE

DATA

STATISTICAL METHODS CONTENTS OF A DATA ANALYSIS PLAN

Narrative analysis seeks to understand the way people create meaning in their lives

Analysing perspectives of marginalised people

Identifying social influence and persuasiveness in events and relationships

Analysing the saliency of political messages from national leaders

Understanding the emotional development through the experiences of children

Depicting a person’s self-identity over a lifespan

Source: Wikipedia

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ANALYSIS OF QUALITATIVE

DATA

STATISTICAL METHODS CONTENTS OF A DATA ANALYSIS PLAN

Discourse analysis focuses on written text; vocal or sign language use; signs, logos or symbols; or significant events (storytelling)

Meaning of gestures, syntax, voice intonation

Government processes and debates of legislation

Corporate marketing messages

Source: Wikipedia

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ANALYSIS OF QUALITATIVE

DATA

STATISTICAL METHODS CONTENTS OF A DATA ANALYSIS PLAN

Framework analysis focuses on combining the results of multiple (often independent) research into a meta- analysis

Determining a comprehensive list of ethical behavior from several case studies of corporate wrongdoing

Combining literature reviews to determine new directions in research

Source: Wikipedia

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ANALYSIS OF QUALITATIVE

DATA

STATISTICAL METHODS CONTENTS OF A DATA ANALYSIS PLAN

Grounded theory involves inducting a general explanation of an event based on data analysis

Understanding the role of therapeutic distance for anxious adults

Understanding organisation culture and co-worker support in corporations

Identifying the quality ‘daily standup meetings on the quality of software developed in agile teams

Determining how a bedside shift report can be used by nurses to keep patients safe

Source: Wikipedia

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ANALYSIS OF QUANTITATIVE

DATA

STATISTICAL METHODS CONTENTS OF A DATA ANALYSIS PLAN

Descriptive analysis

Survey research (interviews, questionnaires, polls) to understand how much or how generalizable is an event or behavior

Comparative analysis

Correlational research tests for the relationships between variables (groups of people or events)

Quasi-experimental analysis

‘Laboratory’ analysis designed to measure a cause-effect relationship between variables

Empirical (experimental) analysis

Statistical testing of hypotheses to determine if a statement or problem is true or untrue

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ANALYSIS OF QUANTITATIVE

DATA

STATISTICAL ANALYSIS TECHNIQUES CONTENTS OF A DATA ANALYSIS PLAN

Descriptive statistics Mean, range, standard deviation, etc., commonly done in MS Excel

The output is typically the charts available in MS Excel

Examples of measures

Frequency: counts, percent

Central Tendency: mean, median, mode

Dispersion or Variation: range, variance, standard deviation

Position: percentile ranks, quartiles, deciles

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ANALYSIS OF QUANTITATIVE

DATA

STATISTICAL ANALYSIS TECHNIQUES CONTENTS OF A DATA ANALYSIS PLAN

MULTI-VARIATE PREDICTIVE TECHNIQUES

Cluster Analysis

customer and demographic segmentation studies

Multidimensional scaling

mapping customer perceptions of multiple brands

Exploratory and Confirmatory Factor Analysis

identifying the rank-order of customer preferences

Structural Equation Modeling

predicting customers’ intention to purchase based on antecedent (e.g. demographic) variables

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SUMMARY

Typical strategy problems deal with understanding the why and how

This is the reason for discussing secondary data and the contents of a Data Analysis Plan

RESEARCH ANALYSIS

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WORKSHOP TIME

Questioning if old measures are relevant to new initiatives

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ARE OLD MEASURES AND ASSUMPTIONS

RECYCLED?

WORKSHOP

In groups or individually, prepare your assessment in Week 9 (45 minutes)

An assessment marking criteria is:

Summarise recommendations that identify areas of focus and opportunity to enhance

Consider how you will validate your recommendations with data analysis

Research questions and/or hypotheses

Dataset(s) for secondary data

Criteria for including and excluding data

Key variables for analysis

Statistical method of choice

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S e e k h e l p w h e n yo u n e e d i t !

Thank you