Business Research Methods_BA306_Summaries

Summary on Chapter 1_Introduction to Research Methods


What is Research (How people visualize research)

i. A situation whereby a scientist has Bunsen burners and test tubes

ii. A situation where an Einstein character is writing dissertations

iii. Collecting data for studies

iv. Research is somewhat intimidating for some people

v. Research itself is a process of finding solutions to a problem

What is Business Research?

• A systematic & organized effort to investigate a specific problem encountered in a work setting that needs a solution. They contain, a) purpose/goals, b)Roles & Responsibilities, c) Ground Rules d) Communication Protocol

• A business research has a process which are inquiries, investigation, examination and experimentation

• It is often undertaken in different business areas including Accounting, Finance, Management, Marketing, etc

Types of Business Research

• Basic/fundamental research: largely an academic exercise

• Applied research: scientific approach used address real life situations

Why Managers should know research

• To identify & effectively solve simple problems in the workplace

• To know how to distinguish between good & bad research

• To take calculated risks in decision making

• To prevent vested parties from influencing a situation unduely

• To appreciate how multiple factors affect a situation

• To relate with hired consultants more effectively

• To combine experience with scientific knowledge

Managing complex problems

To enable him/her resolve unusual problems, the manager is expected to do the following;

• Locate and select a good researcher

• Clearly state the role of the researcher

• Build a strong relationship with the researcher

• Ensure corporate values are in agreement with that of the consultant

Type of researchers

1. Internal researchers

2. External researchers

Ethics in research

Ethics in business research is a code of conduct or expected societal norm or principles guiding the activities of a researcher while conducting research. Therefore, ethical behavior or disposition is expected to reflect at every stage of the research process.


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CHAPTER 2: SCIENTIFIC INVESTIGATION

The hallmark of Science Purposiveness Rigor Testability Replicability Precision and confidence Objectivity Generability Parsimony

Limitations to scientific research in management

• In management and behavioural areas, it is not always possible to conduct investigation that the 100% scientific.

• It is likely to encounter in the measurement and collection of data in the subjective areas of feelings, emotions, attitudes, and perceptions.

• Difficulties in meeting all the hallmark of science in full

The Building of blocks of science Deduction –Conclusion is arrive by logical generalization of known fact Induction – conclusion is arrive by observation of certain phenomena

Hypothetico –deductive method of research

The seven steps of the hypothetico-deductive method Observertion Preliminary information gathering Theory Formulation Hypothesizing Further scientific Data collection Data analysis Deduction



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CHAPTER 4: THE RESEARCH PROCESS - 1

Summary on Chapter 5: The Research Process



A) The need for a theoretical framework (how theories or logical sense of relationships have been identified as important to problem.)


Variables (anything that can take on differing or varying values e.g. production unit, absenteeism, and motivation)


Types


1) The dependent variables (variable of primary interest to researcher)


2) The independent variables (is one that influence the dependent variable in either positive or negative way).


3) Moderating Variable (is one that has a strong contingent effect on the independent variable-dependent variable relationship).


4) Intervening Variables (is one that surfaces between the time the independent variables start operating to influence the dependent


Variable and the time their impact is felt on it)


B) Theoretical Framework and 5 Component of theoretical framework (features).


- Variables consider relevant to the study


- State how two or more variables are related to one another


- The nature and direction of the relationships can be theorized on the basis of the findings of previous research.


- Clear explanations on why these relationship exist)


- A schematic diagram for reader to see and comprehend.


C) Hypothesis Development


-Definition: a logically conjectured relationship between two or more


more variables expressed in the form of a testable statement.


-Hypothesis Statement


-Directional and Non Directional hypothesis

-Null and Altenate Hypothesis

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Summary on Chapter 5: The Research Process


A) The need for a theoretical framework (how theories or logical sense of relationships have been identified as important to problem.)

Variables (anything that can take on differing or varying values e.g. production unit, absenteeism, and motivation)

Types

1) The dependent variables (variable of primary interest to researcher)

2) The independent variables (is one that influence the dependent variable in either positive or negative way).

3) Moderating Variable (is one that has a strong contingent effect on the independent variable-dependent variable relationship).

4) Intervening Variables (is one that surfaces between the time the independent variables start operating to influence the dependent

Variable and the time their impact is felt on it)



B) Theoretical Framework and 5 Component of theoretical framework (features).

- Variables consider relevant to the study

- State how two or more variables are related to one another

- The nature and direction of the relationships can be theorized on the basis of the findings of previous research.

- Clear explanations on why these relationship exist)

- A schematic diagram for reader to see and comprehend.

C) Hypothesis Development

-Definition: a logically conjectured relationship between two or more

more variables expressed in the form of a testable statement.

-Hypothesis Statement

-Directional and Non Directional hypothesis

-Null and Altenate Hypothesis
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Chapter 8_Measurement of Variables: Operational Definition and Scales

What are variables?

A variable is an identity that can assume any value. It is anything or something, whose value or characteristic changes over time. Examples are Turnover, Technology, and GDP etc.

How are variables measured?

While some variables can be measured easily, others are not. How easy a variable can be measured depends on its characteristics and the kind of tools with which it is to be measured.

Types of Variables

i. Those that can be measured objectively and precisely

ii. Those that cannot be measured accurately due to their subjective nature

Operational definition of variables: Dimensions and elements

Dimensions are characteristics, facets or properties attributable to a variable. Behavioral dimensions are crucial for the measurement of variables.

A dimension of a variable is like a thought or an expected characteristic of the said variable. Eg: if the variable being considered is individual’s learning, it is expected that the individual in question will have understanding, retention and be able to apply knowledge etc. Now each of these expectations/characteristics of learning is a dimension and would have elements peculiar to them

For instance, someone with understanding (a dimension of leaning) should be able to answer questions correctly and give appropriate examples (these are elements of the said dimension “understanding” This has been sketched as follows

Dimensions and Elements: illustration


Scales

Scales are tools or mechanisms by which individuals are distinguished as to how they differ from one another on the variables of interest.

Nature of scales

1. Gross scales – that broadly categorize human beings on certain variables

2. Fine-tuned scale – that would differentiate individuals with varying degrees of sophistification

Types of scales

1. Nominal – allows the researcher to assign subjects to certain categories of groups. Eg, Gender (male or female)

2. Ordinal – further to what the nominal scale does, an ordinal scale rank-orders the categories meaningfully. eg, Job characteristics

3. Interval – it allows the researcher to perform certain arithmetical operations on data collected from respondents.

4. Ratio – it overcomes the demerit of arbitrage origin point of the interval scale because it has an absolute zero point, which is a meaningful measure of point.

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Chapter 9_Measurement: Scaling, Reliability, Validity


Scaling is the assignment of numbers or symbols to reveal the attitudinal responses of a subject towards an object, event or person. Types of attitudinal scales are a) the rating scale and b) the ranking scale

Rating scales have several response groups for obtaining responses regarding an object, event or person being studied.

Classes of rating scales

I. Dichotomous scale

II. Category scale

III. Likert scale

IV. Semantic differential scale

V. Itemized rating scale

VI. Fixed or constant sum rating scale

VII. Stapel scale

VIII. Graphic rating scale

IX. Consensus scale

X. Thurstone equal appearing and multidimensional scales are less frequently used


Ranking scales make comparisons between or among objects, events or persons based on their preferred choices and ranking. However, they do not always provide definite clues to the exact needs of the researcher.

To address this problem, alternative methods used are a) paired comparison, b) forced choice, c) comparative scale

Paired comparison – used when respondents are asked to choose between two objects at a time

Forced choice – it enables respondents to rank objects relative to one another ONLY among alternatives provided

Comparative scale – it provides a benchmark for assessing attitudes toward the current object, event or situation under study

Goodness of measures

To obtain results with a high confidence level, the instruments used in the research must measure the variables they are supposed to.

How to ensure good measures

I. Item analysis – to ascertain whether or not the items in instrument belong there. Ensuring round pegs are placed in round holes. Value of t-tests can reveal this.

II. Reliability tests – to ascertain how consistent a measuring instrument does its job. They indicate the stability and consistency of the measurement instruments

III. Validity tests – seeks to ascertain how well the developed instrument measures the exact concept it was intended to measure.

For the mention, the reliability and validity analyses attest to the scientific rigor that has gone into the research study.

Stability of measures – ability of the measure to remain the same over time. The lower the vulnerability, the better. Two tests of stability are as follows

I. Test-retest reliability – the reliability coefficient obtained with a repetition of the same measure even on a second occasion

II. Parallel form reliability – where there is a positive correlation between two comparable sets of measures

Internal consistency of measures – expects that the instruments used in measurement must “hang together as a set” and be capable of independently measuring the same concept accurately. Consistency can be examined using two factors

I. Inter-item consistency reliability – to evaluate how consistent respondents’ responses have been

II. Split-half reliability – reflects the correlation between two halves of an instrument. Estimates obtained will vary to the extent of how the items in the measure have been split into two

Tests for Validity

Validity tests can be grouped under three broad categories

I. Content validity – seeks to ensure the measure includes an adequate and representative set of items that tap the concept.

II. Criterion-related validity – it is established when the measure differentiates individuals based on the criterion it is expected address/predict. This can be done by establishing concurrent validity or predictive validity

a. Concurrent validity is established when the scale discriminates individuals who are already known to be different

b. Predictive validity indicates the ability of the measuring instrument to differentiate among individuals with reference to a future criterion.

III. Construct validity – it attests to how well the results obtained from the use of the measure fit the theories around which the test is designed. This is evaluated using convergent or discriminant validity

a. Convergent validity is established when the scores obtained with two different instruments measuring the same concept are highly positively correlated.

b. Discriminant validity is established when two variables are predicted to be uncorrelated and the results obtained from proved it to be so.


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Chapter 10_DATA COLLECTION METHODS


Data is gathered for the purpose of analysis, testing hypotheses, and answering research questions. The source of the information and the manner in which data are collected could well make a difference to the rigor and effectiveness of the research project,

SOURCES OF DATA

1. Primary data refer to information obtained firsthand and by the researcher on the variables of interest for the purpose of the study. Examples are:

Focus groups are aimed at obtaining respondents’ impressions, interpretations, and opinions and are used for:

• Exploratory studies

• Making generalizations based on the information generated by them

• Conducting sample surveys

Panels are sources of direct information. They could be static or dynamic, and are typically used when several aspects of a topic are to be studied from time to time.

Unobtrusive method originates from a primary source that does not involve people.

2. Secondary data refer to information gathered by someone else other than the researcher conducting the current study. Examples are: books, periodicals, census data, the media, annual reports, etc.

DATA COLLECTION METHODS

The three main data collection methods in survey research are:

1. Interviews

2. Questionnaires

3. Observation

Interviews could be any of the following:

• Structured

• Unstructured

• Face-to-face

• Telephone

• Computer Assisted ( CATI & CAPI)

Training the interviewer

TIPS TO FOLLOW IN INTERVIEWING

• Establishing rapports with the respondents and motivating them to give bias-free responses by allaying whatever suspicions, fears, anxieties, and concerns they may have about the research and its consequences

• Ask broad questions initially and then narrow them to specific areas

• Ask questions in an unbiased way

• Offer clarification when needed

• Help respondents to think through difficult issues

• Responses should be transcribed immediately and not be trusted to memory and later recall.


BIASES IN INTERVIEWS

Biases refer to errors or inaccuracies in the data collected. Biases can be introduced by the following:

• The interviewer

• The interviewee

• The situation


QUESTIONNAIRES

Questionnaires are a pre-formulated written set of questions to which respondents record their answers, usually within rather closely defined alternatives. They can be administered in the following ways:

• Personally

• Mailed

• Electronically distributed

GUIDELINES FOR DESIGNING QUESTIONNAIRES

• Language and wording. This focuses on issues such as type and form of questions i.e., open-ended and closed questions, positively and negatively worded questions.

• Content and purpose of the questions

• Avoiding double-barreled questions, ambiguous questions, leading questions, loaded questions, questions prone to tap socially desirable answers, and those involving distant recall.

• Questions should not be unduly long

• Questions should be well sequenced. Thus the funnel approach will help respondents to progress through the questionnaire with ease and comfort.

PRINCIPLES OF MEASUREMENT

This refers to the scales and scaling techniques used in measuring concepts, as well as the assessment of reliability and validity of the measures used. The interval and ratio scales are preferable to nominal or ordinal scales.

OBSERVATIONAL SURVEY

• Non-participant observer

• Participant- observer

• Structured versus unstructured observation

BIASES IN OBDERVATIONAL STUDIES

• The researchers’ point of view

• The respondents point of view

Data collection through mechanical observation

In this situation, machines provide data by recording events of interest as they occur without a researcher being physically present.

Projective methods

• Word association techniques

• Sentence completion

• Thematic Apperception Tests (TAT)


ADVANTAGES AND DISADVANTAGES OF THE DIFFERENT DATA COLLECTION METHODS

Setting from which data can be gathered

• Natural environment of workplace

• Laboratory

• Homes

• Streets

• Malls

• LAN, etc.



International dimensions of survey

As a result of globalization of business operations, managers often need to compare the business effectiveness of their subsidiaries in different countries. Researchers also engage in cross-cultural research endeavor to trace the similarities and differences in the behavioral and attitidunal responses of employees at different levels in different countries

Special issues in instrumentations for cross-cultural research

• Language uses

Issues in data collection

• Response equivalence

• Timing of data collection

• Status of the individual collecting the data

Managerial advantage

• Knowledge on how to phrase unbiased questions

• Ability to decide the level of sophistication of how data should be collected

• Ability to differentiate between good and bad questions in a survey

Ethics in data collection

• Who sponsors the research

• Who collects data

• Those who offer them

• Respect the confidentiality of data obtained

• Be open minded in accepting the results and recommendations

Ethics and the researcher

• Treating the information given by the respondent as strictly confidential

• The researcher should not misrepresent the nature of the study to subjects

• The self-esteem and self-respect of the subjects should never be violated

• No one should be forced to respond to a survey

• Personal information should not be sort but if absolutely necessary, should be treated with a high level of sensitivity

• Nonparticipant observers should be as nonintrusive as possible

• In lab studies, the subjects should be debriefed with full disclosure of the reason for the experiment

• Subjects should never be exposed to situations where they could be subject to physical or mental harm

• There should be absolutely no misrepresentation or distortion in reporting the data collected during the study

Ethical behaviors of respondents

• The subject should cooperate fully once he has accepted to participate in the study

• The respondent has an obligation to be truthful and honest in the responses given


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CHAPTER 11: Sampling
This chapter seeks to shed more light on sampling as a basic and essential tool in research. It explains how sampling design decisions are important aspects of research design and include both the sampling plan to be used and the sample size that will be needed.

Before going any further, we will need to define/ explain certain terms/words frequently used here;

Population

Population refers to the total number of people, events or things of interest that the researcher wishes to investigate.


Element

An element refers to a single unit of the people, event or things of things of interest in the population.


Population Frame

The population frame is a listing or directory of all the elements that make up the population from which tne sample is drawn.

One of the limitations of the population frame is that it might not always be current or updated.


Sample

A sample is a select group carved out from the population which is going to be used in making generalization on the entire population.
Subject

A subject is a single unit or number from the sample.


Then, what is Sampling?

From the explanations given above, we can refer to sampling as the process or method adopted in creating a select group of subjects to form a sample from the elements in the population.

Why Sampling?

1. Difficulty in gathering information from the entire population

2. The cost implication of gathering data from from the entire population where possible will be very heavy on the researcher

3. There is a tendency of producing more accurate results from sampling rather than the entire population size because of the probability of fewer errors from computing results from a smaller select group.


Types Of Sampling.


There are two major types of sampling designs;

1. Probability Sampling

a. Unrestricted or Simple Random Sampling

b. Restricted or Complex Probability Sampling

i. Systematic sampling

ii. Stratified Random Sampling

iii. Proportionate and Disproportionate Stratified

iv. Cluster Sampling

v. Area Sampling.

vi. Double Sampling.


2. Non-probability Sampling

a. Convenience Sampling

b. Purposive Sampling

i. Judgement Sampling

ii. Quota Sampling.

1.Probability Sampling.

In probability sampling, the elements in the population have a known chance of being selected as sample subjects. This type of sampling is used when the representativeness of the sample is of importance in the interest of the wider generalizability.

Probability sampling can further be broken down into two forms, Restricted or Unrestricted. The Unrestricted or Simple Random Sampling adopts the approach whereby every element in the population has an equal chance of being selected to the sample. However this design could become cumbersome or expensive in a large or complex population hence the development of the Restricted Sampling Design.

The systematic approach involving adopting a unified sequence in choosing subjects from the elements. While the Stratified approach can be adopted in a population whereby the elements in the population have parameters that are segmented or stratified hence he used a systematic design in choosing subjects from the various segments or stratum.

The proportionate or Disproportionate Stratified Sampling design is fallout of the stratified design. Researchers desiring to further create a sample out of each stratum are faced with the challenge of whether to adopt a proportionate or disproportionate design. A proportionate design adopts selecting a unified or proportionate number of subjects from each stratum(eg applying a unified % across of the strata) while the disproportionate adopts a one that isn’t unified.

2. Non-Probability Sampling.

There basically two main types of nonprobability sampling designs: convenience sampling and purposive sampling. Convenience sampling refers to the sampling done with information readily available to the researcher. It usually carried out when quick and timely results are needed. It’s major flaw is that it scores very low in terms of generalization. Purposive Sampling involves sampling from a specific target group and falls into two categories, Judgement and quota sampling design. Judgement sampling though limited in generalization is used when there’s only a select or limited population that can provide information for the research study. While Quota Sampling is adopted when there’s a constraint of either cost, time and the need to adequately represent minority elements in the population.


SAMPLING IN CROSS-CULTURAL RESEARCH.

Cross-Cultural research can basically be defined as the research carried out when comparing or dealing with issues that occur with two or more cultures/ countries/locations involved.

When carrying out sampling in a cross-cultural research the major issue the researcher is faced with is that of the precision and confidence in determining the sample size. Determining the sample size is a major issue any researcher has to deal with when confidently generalizing his/her findings to the population with a high tendency of precision.

What is precision in determining sample size?

Precision refers to how close our estimate is to the population characteristic. In achieving a greater level of precision the researcher has to increase the size of his sample

Confidence?

This refers to how close or certain the researcher is that the estimates will really hold true for the population.

Relationship between Sample Data, Precision & Confidence in Estimation.

The relationship among the sample data , precision & confidence in estimation cannot be overemphasized because the sample data is what is used in making inferences about the population. A good correlation enhances the accuracy of our estimation and in turn increases the confidence of our generalization.

In sum, the sample size is a function of the level of precision and confidence desired

DETERMINING THE SAMPLE SIZE.

The major factors affecting decisions on sample size are as follows;

1. The extent of precision required

2. The acceptable risk in predicting that level of precision

3. The amount of variability in the population itself

4. The cost and time constraints

5. The size of the population itself

Efficiency in Sampling.

Efficiency in sampling is achieved when for a given level of precision, the sample size could be reduced or for a given sample size, the level of precision could be increased.

Team’s Comment.

Members of team two after intensively reviewing this chapter agrees sampling is a very delicate and key aspect of any thorough research work. Identifying the various sampling designs and the appropriateness of each for different research purposes is also very important.

Knowledge gained from this chapter would go along way in improving our efficiency in carrying out a detailed and useful research study.

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CHAPTER 12: DATA ANALYSIS AND INTERPRETATION


After data has been collected from respective sample of the population, the next step is to analyze them to test the research hypothesis.

The Objectives of this chapter are:

a. Edith questionnaire and interview responses

b. Handle blank responses

c. Code the data and set the coding key for the data

d. Categorize data accordingly

e. Create data file for the respective data collected

f. Use appropriate software for data analysis

g. Test the validity of data and

h. Interpret result of various hypotheses.

GETTING DATA READY FOR ANALYSIS

After data are collected through questionnaire, interviews, observation or through secondary sources, these data has to be edited. The blank response has to be handled in some way; the data has to be coded and categorized. Software program like SPSS or Excel or SAS are used for the analysis. The stages for data preparations are:

a. Editing data

b. Handling blank responses

c. Coding

d. Data categorization and

e. Data entering.

DATA ANALYSIS

This involved the use of software programs for the analysis of data collected. The easily available software in business are SPSS and Excel. In data analysis, we have three objectives:

a. Getting a feel of data collected

b. Testing the goodness of data collected, that is, reliability and validity collected can tested or measured.

c. Testing the hypothesis developed for the research

INTERPRETATION OF DATA COLLECTED

Data analysis and interpretation of results of data collected may be explained by referring to a business research project. After a brief description of the background of the company in which the research was carried out and the sample data to be analyzed, the preliminary steps used in the interpretations of data collected are:

a. Checking the Reliability of Measurement using Cronbach’s Alpha reliability coefficient of the independent and dependent variables

b. Obtaining Descriptive Statistics using Frequency Distributions

c. Measures of central tendencies and Dispersion

d. Inferential Statistics using Person Correlation, and

e. Hypothesis Testing.

SOFTWARE PACKAGES USEFUL FOR DATA ANALYSIS
Software useful in the analysis of data collected, questionnaire design, sampling, e-mail surveys, modeling, interactive graphics, web-based questionnaire, and statistical, and chart for presentation are:

a. SPSS software packages

b. Excel packages

c. Askia package

d. ATLAS.ti packages

e. Bellview CATI.

Statistical analysis using spreadsheet like Excel package is different from using statistical package like SPSS package. With Excel, the data and the analysis are both visible to the researchers, whereas SPSS has a separate data file, both the data and the output cannot be displayed at same time.

And important point to note is that data analysis should be based on testing hypothesis that has been already formulated. It would incorrect to change our original hypothesis to suit the result of data analyses. It is however acceptable to develop inductive hypothesis and later test them through further research. We also look at the newly developing software programs that help in questionnaire design and administration.


Broad problem area can be identified through the process of observing and focusing on the situation. The specific issues that need to be researched might fall into:

1.) problem currently existing in an organization

2.) areas that a manager believes need to be improved in the organization

3.) conceptual or theoretical issue that needs to be tightened up for the basic researcher to understand certain phenomena.

Preliminary Data Collection:

Nature of Data to be Gathered:- The nature of information needed by the researcher for the purpose could be broadly classified under three headings:

I. Background information of the organization – that is, the contextual factors like the origin and history of the company, size in terms of employees, location etc.

II. Managerial philosophy, company policies, and other structural aspects.

III. Perceptions, attitudes, and behavioral responses of organizational members and client systems.

Literature Survey:- This is the documentation of a comprehensive review of the published and unpublished work from secondary source of data in the area of specific interest to the researcher. With computerized databases now available and accessible, the literature search is much easier and speedier, and this can even be done without entering the portals of any library. The purpose of literature review is to ensure that no important variable that has in the past been found repeatedly to have had an impact on the problem is ignored.

Based on the specific issues of concern to the manager and the factors identified during the interview process, a literature review needs to be done on these variables. The first step in this process involves identifying the various published and unpublished materials that are available on the topics of interest, and gaining access to them.
In the past, in order to identify relevant sources, researcher needs to manually go through several bibliographical indexes that are compiled periodically, listing the journals, books, and other sources in which published work in the area of interest can be found. Global business information, published articles in newspapers and periodicals among others are all now available on databases. Computerized databases include bibliographies, abstracts, and full-texts database, statistical and financial databases are also easily accessible.

Accessing the online system and getting a printout of all published works in the area of interest from bibliographical index will provide a comprehensive summary of the subject. Whereas, the printout could sometimes include as many as a hundred or more listings, a glance at the titles of the articles or books will indicate which of these may be pertinent and which others are likely to be peripheral to be contemplated.
Writing up Literature Review
The documentation of the relevant studies citing the author and the year of the study is called literature review or literature survey. This survey is a clear and logical presentation of the relevant research work done thus far in the area of investigation. As sated earlier, the purpose of literature survey is to identify and highlight the important variables, and to document the significant findings from earlier research that will serve as the foundation on which the theoretical framework for the current investigation can be built and the hypothesis researcher is knowledgeable about the problem area and has done the preliminary homework that is necessary to conduct the research. A point to note is that literature survey should bring together all relevant information in a cogent and logical manner instead of presenting all the studies in chronological order with bits and pieces of uncoordinated information.
Problem Definition
After the interview and the literature survey, the researcher is in apposition to narrow down the problem from its original broad base and define the issues of concern more clearly. It is critical that the focus of further research be unambiguously identified and defined; there is no amount of research work that can find solutions to the problem if critical issue or problem to be studied is not clearly pinpointed. A problem does not necessarily mean that something is seriously wrong with the current situation that needs to be rectified immediately. A problem could simply indicate an interest in an issue where finding the right answers might help to improve an existing situation.
Examples of well defined problems

i.) to what extent do the structure of the organization and type of information systems installed account for the variance in the perceived effectiveness of managerial decision making?

ii.) how was the new packaging affected the sales of the product?

iii.) Has the new advertising message resulted in enhanced recall?
Managerial Implications
Managers sometimes look at the symptoms in problematic situations and treat them as if they are the real problems, getting frustrated when their remedies do not work. Understanding the antecedents-problem-consequences sequence, and gathering the relevant information to get a real grasp of the problem go a long way in pinpointing it. Managers’ input helps researchers to define the broad problem area and confirm their own theories about the situational factors impacting on the central problems. Managers who realize that correct problem solution, do not grudge the time spent in working closely with researchers, particularly at this stage. Using this facility, the managers can get to know how similar businesses the world over grapple with similar situations and get a better handle on the issues at hand.