smart objectives research paper

Being smart about writing SMART objectives

Affiliations.

• 1 University of North Dakota, School of Medicine & Health Sciences, Center for Rural Health Evaluation, 250 Centennial Dr. Stop 8138, Grand Forks, ND 58202-8138, United States. Electronic address: [email protected].
• 2 University of North Dakota, School of Medicine & Health Sciences, Center for Rural Health Evaluation, 250 Centennial Dr. Stop 8138, Grand Forks, ND 58202-8138, United States. Electronic address: [email protected].
• PMID: 28056403
• DOI: 10.1016/j.evalprogplan.2016.12.009

This article challenges the conventional wisdom in mainstream evaluation regarding the process for developing specific, measurable, attainable, relevant, and time-bound (SMART) objectives. The article notes several advantages of mainstreaming the SMART method including program capacity building and being able to independently monitor progress toward process and outcome objectives. It is argued the one size fits all approach for writing SMART objectives is misleading. The context in which the evaluation is conducted is a key deciding factor in how and when the SMART criteria should be applied. Without an appreciation of the evaluation context, mainstream users may be developing objectives that are far from smart. A case example is presented demonstrating a situation where a stepwise, rather than simultaneous application of the SMART criteria was necessary. Learning from this case, recommendations are forwarded for adjusting how SMART criteria should be presented in mainstream evaluation manuals/guides.

Keywords: Evaluation guidance; Mainstreaming; Objective development; SMART objectives.

Copyright © 2016 Elsevier Ltd. All rights reserved.

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• v.19(1); 2018 Jan

A Randomized Trial of SMART Goal Enhanced Debriefing after Simulation to Promote Educational Actions

Amish aghera.

* Maimonides Medical Center, Department of Emergency Medicine, Brooklyn, New York

† Michigan State University College of Human Medicine, Spectrum Health Emergency Medicine Residency, Grand Rapids, Michigan

Richard Bounds

‡ University of Vermont Medical Center, Division of Emergency Medicine, Department of Surgery, Burlington, Vermont

Colleen Bush

R. brent stansfield.

§ Wayne State University School of Medicine, Detroit, Michigan

Brian Gillett

Sally a. santen.

¶ Virginia Commonwealth University School of Medicine, Richmond, Virginia

Associated Data

Introduction.

Goal setting is used in education to promote learning and performance. Debriefing after clinical scenario-based simulation is a well-established practice that provides learners a defined structure to review and improve performance. Our objective was to integrate formal learning goal generation, using the SMART framework (Specific, Measurable, Attainable, Realistic, and Time-bound), into standard debriefing processes (i.e., “SMART Goal Enhanced Debriefing”) and subsequently measure the impact on the development of learning goals and execution of educational actions.

This was a prospective multicenter randomized controlled study of 80 emergency medicine residents at three academic hospitals comparing the effectiveness of SMART Goal Enhanced Debriefing to a standard debriefing. Residents were block randomized on a rolling basis following a simulation case. SMART Goal Enhanced Debriefing included five minutes of formal instruction on the development of SMART learning goals during the summary/application phase of the debrief. Outcome measures included the number of recalled learning goals, self-reported executed educational actions, and quality of each learning goal and educational action after a two-week follow-up period.

The mean number of reported learning goals was similar in the standard debriefing group (mean 2.05 goals, SD 1.13, n =37 residents), and in the SMART Goal Enhanced Debriefing group (mean 1.93, SD 0.96, n =43), with no difference in learning goal quality. Residents receiving SMART Goal Enhanced Debriefing completed more educational actions on average (Control group actions completed 0.97 (SD 0.87), SMART debrief group 1.44 (SD 1.03) p =0.03).

The number and quality of learning goals reported by residents was not improved as a result of SMART Goal Enhanced Debriefing. Residents did, however, execute more educational actions, which is consistent with the overarching intent of any educational intervention.

INTRODUCTION

In education, a critical step facilitating the transfer of lessons learned into practice is creating action plans or setting learning goals. 1 , 2 While goals are not always accomplished, there is a clear relationship between setting goals and achievement. 3 , 4 Goals can influence performance by focusing effort and attention to a specific domain resulting in greater effort and persistence of effort, as well as strategies to approach tasks. 3 – 5 An established model for developing actionable learning goals is the “SMART” framework. These goals are Specific, Measurable, Attainable, Realistic, and Time-bound. The SMART framework is easy to teach, easy to remember, and has been employed successfully across multiple disciplines, including medical education. 6 – 12 Ideally, SMART goals consist of practical, concrete actions that learners plan to implement to improve their knowledge, skills, and attitudes, with an emphasis on tangible outcomes. 7 , 9 , 13

It is commonly held that residents will form learning goals without prompting and then execute them; however, this assumption is untested. While formal goal-setting instruction improves the quality of resident-generated learning goals, learners may struggle to independently create high-quality goals due to problems inherent in self-assessment. 14 – 17 However, the practice of self-assessment has been shown to generate a greater number of learning goals, and these goals are more likely to be carried out. 8 , 18

As an educational platform in healthcare, simulation-based medical education (SBME) lends itself as a strategy for pairing informed self-assessment and targeted goal setting. SBME employs well-structured, guided debriefing sessions incorporating formative feedback to impact performance. 19 – 23 Debriefing strategies are designed to engage learners through a reflective conversation using objective feedback and self-assessment, thereby providing the context to change suboptimal practice patterns and improve patient outcomes. 24 However, all debriefing techniques do not incorporate the generation of explicit learning goals. 25 The use of debriefing in SBME as a vehicle to impact educational outcomes by providing informed self-assessment in conjunction with explicit goal-setting warrants further study.

The objective of our study was to compare the effectiveness of a novel debriefing modality that integrated the creation of quality, self-directed learning goals identified from a clinical simulation scenario, compared to a standard simulation debriefing without explicit dialogue about learning goals. We hypothesized that this “ SMART Goal Enhanced Debrief ” would result in the completion of a greater number and higher quality of learning goals and educational actions.

Study Design

This was a prospective multicenter randomized controlled study comparing the effectiveness of a standard debriefing process to SMART Goal Enhanced Debriefing, which employed the use of coaching to develop “SMART” learning goals. 9 Learners participated in a high-fidelity, mannequin-based clinical simulation scenario followed by formal debriefing with one of two methods. Measured outcomes included both the generation of learning goals and the subsequent completion of educational actions. The study was approved by each institution’s local institutional review board and classified as exempt at each site (i.e., informed consent was not required in accordance with standard educational practices).

Population Health Research Capsule

What do we already know about this issue?

Goals help to promote learning and performance. The “SMART” (Specific, Measurable, Attainable, Realistic, Time-bound) framework for setting goals has been successfully used across multiple disciplines including medicine.

What was the research question?

To evaluate the effectiveness of a SMART Goal Enhanced Debriefing strategy after simulation.

What was the major finding of the study?

SMART Goal Enhanced Debriefing stimulated additional self-directed learning through executed educational actions.

How does this improve population health?

Improving debriefing methodology after simulation has the potential to reach a wide variety of learners across the healthcare continuum.

Study Setting and Sample

The study was conducted at three academic hospitals from November 2013 to March 2014, each supporting Accreditation Council for Graduate Medical Education approved residencies in emergency medicine (EM). Attributes include one Midwest urban university affiliated site with an annual emergency department (ED) census of 110K visits (Site1); one Mid-Atlantic suburban university affiliated site with an ED volume of 115K visits (Site 2); and one Northeast private urban site with an annual ED census of 120K visits (Site 3). Respectively, each site supports nine EM residents/year, 12 EM or combined program EM/family practice or EM/internal medicine residents/year, and 16 EM residents/year. Subjects included a convenience sample of EM residents or combined program residents. Participation in the study was voluntary, though residents were required at their respective institutions to routinely participate in simulation-based educational activities as part of general curricular requirements.

We determined necessary sample size based on estimated number of educational actions that would be reported in the control and intervention groups, based on the study team’s previous experience in this area. 8 Initially, the need for 88 residents was predicted based on an estimate of 0.8 reported actions in the control group, and 2.0 in the intervention group (standard deviation [SD] 2, alpha 0.05, power 80%, enrollment ratio 1). We terminated enrollment early due to achieving statistical significance between the two groups.

Study Protocol

Simulation case scenarios.

A schematic of the study protocol is graphically represented in Figure 1 . Case scenarios were not standardized across institutions in order to model typical educational settings representing the variety of cases used for teaching. Recognizing that certain types of cases may lend themselves better as a stimulus for generating goals and actions, residents were block randomized by case at each site. The priority of randomization was to have a similar spread of cases across both groups. Program administrators did appropriately match resident postgraduate year (PGY) level to specific case scenarios and associated learning objectives in advance. Cases at each site involved the participation of two or three residents. Residents were enrolled only once and were blinded to their assigned group.

Schematic of study protocol comparing development of learning goals.

After completing the simulation, residents received approximately 30 minutes of debriefing time structured as a standard debrief (control group), or a SMART Goal Enhanced Debrief, which embedded five minutes of formal instruction and development of SMART learning goals (intervention group). Of note, the length of time for case scenarios and debriefing were constrained by each site’s curricular structure, and thus any individual group did not receive any more or less instruction time in total. Residents were asked to keep scenario details confidential to allow cases to remain novel for future participants.

EM academic faculty members with experience in simulation debriefing facilitated the simulation sessions. Faculty members were not limited to members of the study team or participation in either the control or intervention groups. However, to minimize the effect of varying debriefing styles each facilitator was trained to assure that each debriefing session was conducted in a well-accepted and structured format consisting of three phases: reactions, analysis/reflection, and summary/application ( Appendix 1 ). 24 It is important to note that facilitators would still routinely discuss lessons learned and next steps in the summary/application phase of the debrief as part of standard practice in the control group. The enhancement of this practice in the intervention group specifically related to coaching and writing down goals in the SMART format during this final debriefing phase.

SMART Goal Enhanced Debriefing

In the intervention group, education around the development of SMART learning goals was conducted in the summary/application phase of the debriefing to facilitate linking lessons learned from the case to explicit goals. Faculty instructors guided residents to generate SMART learning goals in response to the simulation, using a standardized worksheet that defined SMART learning goals with examples ( Appendix 2 ). Residents were allowed to keep the worksheet after the debriefing.

Evaluation of Debriefing

At the conclusion of each debriefing session, residents were asked to complete the Debriefing Assessment for Simulation in Healthcare (DASH) for the purpose of monitoring the overall quality of SBME sessions in both study groups. Residents were given the “DASH – Student Version Short Form,” which is designed for learners to rate their instructors in each of the six core DASH elements in less than three minutes. 26 Content validity of the DASH has its basis in best debriefing practices defined by an expert panel grounded in an extensive literature review. 27

Measurements

The primary outcome was to compare the number and quality of learning goals and educational actions recalled after a two-week follow-up interval by residents after standard debriefing (control group) to the learning goals and educational actions recalled by resident’s who underwent SMART Goal Enhanced Debriefing (intervention group). Specifically, all residents were asked to list learning goals and educational actions taken in response to their simulation case encounter ( Appendix 3 ). A two-week time interval was chosen because the study team felt that it would be unlikely for educational actions to be executed beyond that time frame. Additionally, minimizing the follow-up period would help limit recall bias.

Learning Goal Rating Scale – Validity Evidence

Initially, we rated the quality of learning goals using a scoring rubric with validity evidence published by Lockspeiser, 28 which was subdivided into domains based in the “I-SMART” mnemonic (i.e., important, specific, measureable, etc.). Unfortunately, raters in this study could not reliably apply Lockspeiser’s rubric to the recalled goals submitted by our cohort of learners. As a result, we created a modified Learning Goal Rating Scale ( Figure 2 ). To support content validity, we adapted Lockspeiser’s original anchors that uniquely related to the “SMART” criteria within the context of our learning-goal worksheet. Response process was improved through an iterative process of rater training and tool refinement. Developing general scoring guidelines and streamlining the tool into a single global rating scale decreased variation in interpreting the anchors.

Learning goal and educational action rating instruments.

Internal structure of the Learning Goal Rating Scale was supported by measuring an intraclass correlation coefficient (ICC), using a two-way model estimating the reliability of average κ ratings. Upon finalizing the structure of the Learning Goal Rating Scale, four members of the study team used it to independently rate a representative subset of learning goals ( n =21) with good reliability (ICC=0.82). Once this initial reliability was established, the same four members of the study team applied the Learning Goal Rating Scale to every reported learning goal ( n =155). We found that good reliability was maintained (ICC=0.78). The Learning Goal Rating Scale was not tested for relationships to other variables or consequences.

Educational Action Rating Scale – Validity Evidence

We measured the quality of the educational actions using an Educational Action Rating Scale ( Figure 2 ). It was developed de novo as there was no existing instrument for this purpose. To support content validity, we chose rating criteria based on principles of education pedagogy such as the cognitive domain of Bloom’s Taxonomy. 29 , 30 In essence, higher ratings would be given to activities that incorporated active learning and were deemed more relevant to clinical practice. Furthermore, given that the amount of time spent engaged in a learning activity correlates with educational impact, duration of the activity would also result in an improved rating. To support response process validity, the instrument was piloted and revised using an iterative process to simplify the interpretation of specific rating criteria. Initially, four members of the study team rated a representative subset of educational actions from our cohort ( n =18) with good ICC (0.86). At three months, excellent test-retest reliability was demonstrated on the same subset of educational actions (ICC=0.94). Follow-up ratings of every educational action ( n =95) by the same four raters revealed good ICC (0.90). The Educational Action Rating Scale was not tested for relationships to other variables or consequences.

Average Quality Ratings

Learning goal and educational action ratings were performed by four study investigators blinded to study site and group (control or intervention). Each study investigator rated the quality of reported goals and actions for all study subjects. We created the Average Learning Goal Quality by averaging ratings of learning goals within each study group. The Average Educational Action Quality was calculated in a similar manner.

Data Analysis

We evaluated sampling distribution of simulation cases using a chi-squared test, or Fisher’s exact test when the case frequency was <5 in any group. A p value of <0.05 was considered significant. We used descriptive statistics to summarize the number and quality of goals and educational actions. The number and quality of learning goals and educational actions from the control and intervention groups were compared using a t-test. We summarized DASH results with descriptive statistics and applied t-tests to determine statistically significant differences in the delivery of SBME sessions between groups. A p < 0.05 level was considered significant.

A total of 80 residents were enrolled in the study: 37 in the standard debriefing (control) group, and 43 in the SMART Goal Enhanced Debriefing (intervention) group. A breakdown of the PGY level of study subjects in each group and site are detailed in Tables 1 and ​ and2. 2 . Table 3 lists simulation case scenarios, their frequency of utilization, and a statistical measure of randomization.

Subjects in the standard debriefing group.

PGY , post graduate year.

Subjects in the SMART Goal Enhanced Debriefing group.

PGY , post graduate year; SMART , specific, measurable, attainable, realistic and time-bound.

Clinical simulation case scenarios and frequency.

TCA , tricyclic antidepressant.

Residents in the standard debriefing group ( n =37) recalled a total of 76 learning goals and subsequently reported 36 educational actions performed. Residents in the SMART Goal Enhanced Debriefing group ( n =41) recalled 79 goals and reported 59 actions performed. Two PGY1 residents in the SMART Goal Enhanced Debriefing group were lost to follow-up at Site 3 (did not return/submit their learning goals and action items).

The mean number and quality of learning goals recalled and educational actions reported are detailed in Table 4 . There was no significant difference in the mean number of goals reported or goal quality; however, residents receiving SMART Goal Enhanced Debriefing completed more educational actions on average ( p =0.03). There was no difference in action quality.

Mean and standard deviation of number and quality of learning goals and educational actions.

SD , standard deviation; SMART , specific, measurable, attainable, realistic, time-bound.

We reviewed the DASH ratings of the simulation sessions in both groups to ensure that the quality of debriefing was similar in both groups. Both were rated similarly across all measured domains ( Table 5 ).

Mean and standard deviation of resident “DASH” ratings (Debriefing Assessment in Healthcare). Ratings are all reported on a scale of 1 to 7 (1=extremely ineffective, 7=extremely effective).

The ability to efficiently engage in goal-oriented, self-directed learning has the potential to serve as a scaffold for ongoing performance improvement over the entirety of a physician’s career. Widespread application of deliberate goal setting should be considered an important skill to promote ongoing professional development. In this study, SBME motivated residents to set learning goals after both standard debriefing as well as SMART Goal Enhanced Debriefing. Residents did not generate more learning goals as a result of receiving SMART Goal Enhanced Debriefing. Notably, residents from this group reported performing more educational actions, which is arguably the more important metric related to improving one’s clinical performance.

We theorize that the process of creating SMART learning goals served as a subconscious primer for the execution of goals. Priming is thought to improve the likelihood of one’s acting on a goal by increasing motivation, focus, and commitment. 3 , 31 Concurrently, automatic goal activation can be influenced by associations with situational features and mental representations of colleagues’ goal pursuits. 32 Both of these factors likely came into play in our study. For example, a key situational feature was the explicit use of SMART learning-goal worksheets, while debriefing with peers and instructors provided external mental representations of the goals of others.

Other educational factors may also have worked in combination, or even synergistically, to promote the execution of goals. For example, all simulation debriefings in our study used the technique of summarizing lessons learned in relation to observed performance. When explicitly linked with the development of learning goals, this technique may have served as a powerful stimulus to promote the completion of subsequent learning activities. 5 Further codifying learning goals into the structured SMART framework may also have stimulated ongoing motivation such that even more actions were completed in the intervention group. Theoretical constructs in goal-setting supporting motivation include improving affect (i.e., feels good to achieve a goal); metacognition (i.e., stimulation of task strategies for goal attainment); and choice (i.e., learner-centered goals are more likely to be pursued). 4 , 5

Regardless of the underlying mechanism, we believe that equipping learners with an explicit method to develop focused learning goals may help them become self-directed learners. This is particularly valuable in the context of SBME, which is a commonly employed educational technique across the healthcare continuum. Regardless of profession, simulation educators craft clinical cases and debriefing objectives tailored to their learners. Debriefing incorporates self-assessment and reflection as key components that impact the learning process. Building on this framework, improving a learners’ ability to create actionable learning goals will ultimately facilitate improvement in subsequent clinical performance. In our experience, instructors can become skilled at applying the SMART goal format in a short time period.

LIMITATIONS

There are several limitations to this study. We chose to study our intervention with non-standardized simulation case scenarios to replicate conditions in routine educational settings in the hopes of making our findings more generalizable. While we asked all residents to self-report their learning goals and actions approximately two weeks after the educational encounter, it is difficult to know if residents accurately represented these goals and actions in follow-up. There may be an effect of recall bias. Finally, novel measurement tools were developed in an effort to quantify the quality of goals and actions. We recognize that our interpretations cannot be “fully valid.” 33 As a result, validity evidence was collected during the development of the measurement tools. This resulted in a process of refinement of a Learning Goal Rating Scale. Similarly, development of the Educational Action Rating Scale was developed de novo and has not been validated externally. The impact on study results are unknown.

We found that debriefing after simulation is an effective modality to stimulate the development of learning goals and the execution of educational actions. While the application of a simple goal-setting exercise (i.e., SMART Goal Enhanced Debriefing) did not increase the number and quality of goals recalled, it did serve as a powerful primer to promote additional self-directed learning through executed educational actions. This intervention can be readily applied to most simulation debriefing sessions and requires little training to be employed effectively.

Supplementary Information

Section Editor: Andrew W. Phillips, MD

Full text available through open access at http://escholarship.org/uc/uciem_westjem

Conflicts of Interest : By the West JEM article submission agreement, all authors are required to disclose all affiliations, funding sources and financial or management relationships that could be perceived as potential sources of bias. No author has professional or financial relationships with any companies that are relevant to this study. There are no conflicts of interest or sources of funding to declare.

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• Writing Tips

How to Write Research Objectives

3-minute read

• 22nd November 2021

Writing a research paper, thesis, or dissertation ? If so, you’ll want to state your research objectives in the introduction of your paper to make it clear to your readers what you’re trying to accomplish. But how do you write effective research objectives? In this post, we’ll look at two key topics to help you do this:

• How to use your research aims as a basis for developing objectives.
• How to use SMART criteria to refine your research objectives.

For more advice on how to write strong research objectives, see below.

Research Aims and Objectives

There is an important difference between research aims and research objectives:

• A research aim defines the main purpose of your research. As such, you can think of your research aim as answering the question “What are you doing?”
• Research objectives (as most studies will have more than one) are the steps you will take to fulfil your aims. As such, your objectives should answer the question “How are you conducting your research?”

For instance, an example research aim could be:

This study will investigate the link between dehydration and the incidence of urinary tract infections (UTIs) in intensive care patients in Australia.

To develop a set of research objectives, you would then break down the various steps involved in meeting said aim. For example:

This study will investigate the link between dehydration and the incidence of urinary tract infections (UTIs) in intensive care patients in Australia. To achieve this, the study objectives w ill include:

• Replicat ing a small Singaporean study into the role of dehydration in UTIs in hospital patients (Sepe, 2018) in a larger Australian cohort.
• Trialing the use of intravenous fluids for intensive care patients to prevent dehydration.
• Assessing the relationship between the age of patients and quantities of intravenous fluids needed to counter dehydration.

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Note that the objectives don’t go into any great detail here. The key is to briefly summarize each component of your study. You can save details for how you will conduct the research for the methodology section of your paper.

Make Your Research Objectives SMART

A great way to refine your research objectives is to use SMART criteria . Borrowed from the world of project management, there are many versions of this system. However, we’re going to focus on developing specific, measurable, achievable, relevant, and timebound objectives.

In other words, a good research objective should be all of the following:

• S pecific – Is the objective clear and well-defined?
• M easurable – How will you know when the objective has been achieved? Is there a way to measure the thing you’re seeking to do?
• A chievable – Do you have the support and resources necessary to undertake this action? Are you being overly ambitious with this objective?
• R elevant – Is this objective vital for fulfilling your research aim?
• T imebound – Can this action be realistically undertaken in the time you have?

If you follow this system, your research objectives will be much stronger.

Expert Research Proofreading

Whatever your research aims and objectives, make sure to have your academic writing proofread by the experts!

Our academic editors can help you with research papers and proposals , as well as any other scholarly document you need checking. And this will help to ensure that your academic writing is always clear, concise, and precise.

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Using SMART Goals to Make Scientific Progress

By Alex Szatmary

Thursday, July 14, 2016

My well-organized desk, where SMART goals get completed.

A timer is always running on my desk, as I try to complete my current task before it beeps. This makes me sound like an organized, competitive, methodical person—not so! My desk is covered with mugs, stacks of paper, and cables unneeded for years. Email sits unreplied-to for days.

As a theoretician in the lab of Dr. Ralph Nossal (NICHD), I use mathematical modeling to study how cells get to places in the body. Most of my time is focused on completing clearly written goals born from project plans. A system of timers, project plans, and goals keeps me on track to do what I need to do so that I can get back to the fun part of my job that I would happily do for free. 

Here is some background information on my research project: When a person experiences inflammation, the body directs white blood cells to the inflammation site. In a process called chemotaxis, white blood cells migrate along a gradient of signaling molecules. During a bacterial inflammation, molecules made by the bacteria spread from the inflammation site. When white blood cells sense bacterial molecules, they secrete their own molecule called leukotriene B4 (LTB 4 ). White blood cells can navigate based on signals received from bacterial molecules, as well as by responding to LTB 4 . My overarching goal as a member of Dr. Nossal’s team is to determine how LTB 4 helps white blood cells coordinate their motion, which may clarify the ways various moving cells communicate in other contexts, such as during development and cancer metastasis.

Upon entering the body, bacterial molecules (black circles) stimulate a nearby population of white blood cells (grey objects) to generate LTB 4 (red triangles); the LTB 4 reaches other white blood cells that are too far from the bacteria to sense them directly, enhancing the immune system’s response.

A typical research project requires me to:

• read scientific literature on cell migration and signaling
• write computer code to model cell motion and communication
• communicate with experimentalists to determine what measurements we need
• gather, plot, and analyze the data
• collaborate with colleagues to write a paper that will undergo peer review
• manage the paper through the peer-review process

One of my challenges is that I like writing code more than I like writing prose, and so I can spend a long time working with nothing publishable to show. Writing goals and scheduling my time helps me bridge that gap between purpose and results, to make the most productive use of my time as an IRP postdoc. I wanted to make sure that I limit my coding efforts to what will be relevant to the paper and make steady progress on writing the paper.

Writing and pursuing goals can waste valuable time if the goals are not good. What makes a goal ‘good’? When writing goals, I’ve learned to use the ‘SMART’ criteria to ensure that it’s posed in a way that will move our research efforts forward.

SMART goals are:

In the business world, George T. Dolan pioneered the idea of setting SMART goals back in 1981 (1) . Since then, multiple authors have adapted his concepts to setting objectives for project management and personal development (2) .

Examples of how I employ SMART goals in scientific research:

Goals should not be ambiguous. First, I write down my overall goal and describe precisely what I’m trying to achieve. What do I want to accomplish?

I avoid goals like “Make plots,” because that’s a big, complex goal. It’s better for me to focus by breaking goals down into smaller, targeted parts:

• “Make plots showing how LTB4 concentration varies over time.”
• “Arrange plots to compare cell motion with and without LTB4.”
• “Fix problems noted by colleagues in draft.”

I clarify my specific outcome before I start, which lets me focus on the “what” rather than the “why” of what I’m doing, while I’m doing it.

How do I know when a goal is complete? By evaluating my progress. Each goal I write has a series of objectives that help me make small steps toward achieving the overall goal. These objectives are precise, concrete, and measurable.

Questions I ask when writing my goals include:

• “How am I going to accomplish this goal?”
• “What will I do or learn in the process?”
• To figure out if a goal is measurable, I ask, “How will I know when this is done?”

A goal like “Read papers on chemotaxis” can never be completed— a quick search of PubMed for ‘chemotaxis’ pulls up 36,039 papers . On the other hand, “Read three review papers on chemotaxis” is something I can do this afternoon if I start now. In scientific writing, goals that include word counts can help, because they’re objective and precise, but I prefer goals like, “Write paragraph on results for cell recruitment in early inflammation.”

Measurable doesn’t have to mean completely objective; a goal only has to be clear enough for me to know when I’m making progress on it and when it’s time to stop and do something else.

Who is responsible for making the goal happen? Are expectations clear and agreed upon by all interested parties? On a team collecting and analyzing data, it’s important to identify not just who has which role, but what condition the data should be in when it’s passed from the collectors to the analysts.

Most of the goals I write are assigned to me, but I also record to-dos to remind me to check on things I have asked others to do:

• “Who said they would give me feedback on my paper, and by when? Do they have everything they need?”
• “Did that order for printer toner get made? If not, what needs to happen?”
• “Has my summer student completed a draft of his poster?”

Can I achieve this goal with my current skills and resources? If not, is it feasible to acquire the necessary skills and resources in the goal’s established time frame? Is the time frame appropriate to the complexity and amount of effort the goal requires?

Goals are made to be achieved:

• “Write subsection on modeling LTB 4 transport today” is doable.
• “Write methods section this week” could be realistic, but “Write results and discussion today” probably is not.

Writing unrealistic goals leaves me discouraged when I don’t meet them, so I write goals that I’m confident that I can accomplish. Having a realistic plan lets me tell my collaborators when they can count on having things finished.

I establish a timeline for completing each goal and assessing progress. Is the timeline relevant to my current deadlines, and does it reflect my long-term objectives?

Many goals have a deadline built in, sometimes recurring. For example, I have a poster session coming up at the NIH Research Festival , and I need to prepare weekly lessons for a class I’m teaching. Some projects don’t have a hard deadline, but if I feel like I need to rush to finish a paper before I send out a grant application, it might be too late already.

Similarly, making a career move takes lots of preparation. I’m in the middle of my search for a tenure-track position at a predominantly undergraduate institution. To figure out my career goal, I arranged informational interviews with people in my network. I took workshops on teaching and then taught a class twice through FAES . Then, I prepared a job package and improved it with feedback from friends and mentors. All that could not have been done in the last few months of a fellowship.

I regularly review my plans to clarify what I need to do now rather than next week, even for projects that seem open-ended. It’s fine to have big goals like “submit paper before November,” but I usually break large goals down into things that can be finished in 30 minutes to four hours of work. Tasks much shorter than half an hour can actually take more time to keep track of than to do, so I group related short tasks into a single goal. On the other hand, gauging progress on goals that take more than half a day can be difficult, unless broken down into smaller steps.

Below is an example of my goal tracking in practice, with some notes included on why one of my goals was not achievable as written:

I don’t plan projects and keep track of goals because this way of thinking comes easily to me; I have to track goals explicitly, because I don’t automatically know what needs doing. Benefits of using the SMART criteria when planning and assessing goals include:

• Making it easy for me to figure out what to do next
• Determining what doesn’t really need doing, or what doesn’t need doing right now
• Managing expectations with my mentor and co-workers
• Sensing when it’s time to take a break or work on a fun side project

Research feels slow sometimes. It can also feel intimidating to start writing a manuscript. One of the most satisfying things to me about tracking goals is that, when I feel like I’m not making progress fast enough, I can look at my records and see how much I’ve actually accomplished.

This spring, I used SMART goals to lay out what is needed to turn my modeling work into a paper. Specific, measurable, and assignable goals helped my collaborators understand what data I needed from them on how neutrophils secrete LTB 4 , which also helped them predict what data they would have to present at a conference. Realistic and time-bound goals clarified our options as we decided which hypotheses to test. Two months later, we have drafted a paper we are close to submitting for review.

Thank you to Jennifer Patterson-West for contributing significant efforts to this post.

1. Doran, G. T. (1981). "There's a S.M.A.R.T. Way to Write Management's Goals and Objectives", Management Review, Vol. 70, Issue 11, pp. 35-36. 2. Fuhrmann, C.N., Hobin, J.A., Clifford, P.S., and Lindstaedt, B. (2013) “Goal-Setting Strategies for Scientific and Career Success.” Science Careers. http://sciencecareers.sciencemag.org/career_magazine/previous_issues/articles/2013_12_03/caredit.a1300263

Additional Resources

• http://cds.sdce.edu/decision-making/SMART-Goal-Setting
• This webpage has some useful worksheets for setting S.M.A.R.T. goals and provides a more detailed description of the concept.
• http://professional.opcd.wfu.edu/files/2012/09/Smart-Goal-Setting.pdf

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Work Life is Atlassian’s flagship publication dedicated to unleashing the potential of every team through real-life advice, inspiring stories, and thoughtful perspectives from leaders around the world.

Contributing Writer

Work Futurist

Senior Quantitative Researcher, People Insights

Principal Writer

How to write SMART goals

It’s easier to succeed when you have clearly defined objectives that are based in reality.

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5-second summary

• Teams often fall short of meeting their goals due to a lack of consensus on the definition of success.
• SMART goals use a specific set of criteria to help ensure that objectives are clearly defined and attainable within a certain timeframe.
• Working through each step of creating a SMART goal can reveal instances where priorities and resources are out of alignment.

Meet Jane. She’s a product manager at a mid-sized tech company – let’s call it Techfirm, Inc. Jane has been tasked with increasing usage of Techfirm’s mobile app.

She knows she’ll need all hands on deck to make this happen, but when Jane has set team-wide goals in the past, they’ve quickly fallen off track. Nobody seemed to have a clear understanding of what success should look like; progress wasn’t monitored closely enough, and inevitably, that important objective slipped to the back burner (before toppling off the stove entirely).

That’s why, this time around, Jane plans to leverage SMART goals for setting an action plan and staying the course.

Want to get started right now?

Use our template to define the different components of your SMART goal.

What are SMART goals?

The SMART in SMART goals stands for Specific, Measurable, Achievable, Relevant, and Time-Bound.

Defining these parameters as they pertain to your goal helps ensure that your objectives are attainable within a certain time frame. This approach eliminates generalities and guesswork, sets a clear timeline, and makes it easier to track progress and identify missed milestones.

An example of a SMART-goal statement might look like this: Our goal is to [quantifiable objective] by [timeframe or deadline]. [Key players or teams] will accomplish this goal by [what steps you’ll take to achieve the goal]. Accomplishing this goal will [result or benefit].

Let’s use Jane’s objective to work through each component.

S: Specific

In order for a goal to be effective, it needs to be specific. A specific goal answers questions like:

• What needs to be accomplished?
• Who’s responsible for it?
• What steps need to be taken to achieve it?

Thinking through these questions helps get to the heart of what you’re aiming for. Here’s an example of a specific goal Jane might come up with:

Grow the number of monthly users of Techfirm’s mobile app by optimizing our app-store listing and creating targeted social media campaigns.

M: Measurable

Don’t underestimate the outsized impact of short-term goals

Specificity is a solid start, but quantifying your goals (that is, making sure they’re measurable) makes it easier to track progress and know when you’ve reached the finish line.

Jane and her product team want to grow the number of their mobile app users – but by how much? If they get even one new signup, that’s technically positive growth – so does that mean they’re done? Same goes for their strategy – how many platforms will they advertise on? 

To make this SMART objective more impactful, Jane should incorporate measurable, trackable benchmarks.

Increase the number of monthly users of Techfirm’s mobile app by 1,000 by optimizing our app-store listing and creating targeted social media campaigns for four social media platforms: Facebook, Twitter, Instagram, and LinkedIn.

A: Achievable

This is the point in the process when you give yourself a serious reality check. Goals should be realistic –  not  pedestals from which you inevitably tumble. Ask yourself: is your objective something your team can reasonably accomplish?

Jane might look at her goal and realize that, given her small team and their heavy workload, creating ad campaigns for four social platforms might be biting off more than they can chew. She decides to scale back to the three social networks where she’s most likely to find new clients.

Increase the number of monthly users of Techfirm’s mobile app by 1,000 by optimizing our app-store listing and creating targeted social media campaigns for three social media platforms: Facebook, Twitter, and Instagram.

Safeguarding the achievability of your goal is much easier when you’re the one setting it. However, that’s not always the case. When goals are handed down from elsewhere, make sure to communicate any restraints you may be working under. Even if you can’t shift the end goal, at least you can make your position (and any potential roadblocks) known up-front.

R: Relevant

Here’s where you need to think about the big picture. Why are you setting the goal that you’re setting? Jane knows that the app is a huge driver of customer loyalty, and that an uptick in their app usage could mean big things for the company’s bottom-line revenue goals. Now she revises her statement to reflect that context.

Grow the number of monthly users of Techfirm’s mobile app by 1,000 by optimizing our app-store listing and creating targeted social media campaigns for three social media platforms: Facebook, Twitter, and Instagram. Because mobile users tend to use our product longer, growing our app usage will ultimately increase profitability.

T: Time-bound

To properly measure success, you and your team need to be on the same page about when a goal has been reached. What’s your time horizon? When will the team start creating and implementing the tasks they’ve identified? When will they finish?

SMART goals should have time-related parameters built in, so everybody knows how to stay on track within a designated time frame.

When Jane incorporates those dates, her SMART goal is complete.

Grow the number of monthly users of Techfirm’s mobile app by 1,000 within Q1 of 2022. This will be accomplished by optimizing our app-store listing and creating targeted social media campaigns, which will begin running in February 2022, on three social media platforms: Facebook, Twitter, and Instagram. Since mobile is our primary point of conversion for paid-customer signups, growing our app usage will ultimately increase sales.

Knowing how to set goals using the SMART framework can help you succeed in setting and attaining goals, no matter how large or small.

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• Aims and Objectives – A Guide for Academic Writing
• Doing a PhD

One of the most important aspects of a thesis, dissertation or research paper is the correct formulation of the aims and objectives. This is because your aims and objectives will establish the scope, depth and direction that your research will ultimately take. An effective set of aims and objectives will give your research focus and your reader clarity, with your aims indicating what is to be achieved, and your objectives indicating how it will be achieved.

Introduction

There is no getting away from the importance of the aims and objectives in determining the success of your research project. Unfortunately, however, it is an aspect that many students struggle with, and ultimately end up doing poorly. Given their importance, if you suspect that there is even the smallest possibility that you belong to this group of students, we strongly recommend you read this page in full.

This page describes what research aims and objectives are, how they differ from each other, how to write them correctly, and the common mistakes students make and how to avoid them. An example of a good aim and objectives from a past thesis has also been deconstructed to help your understanding.

What Are Aims and Objectives?

Research aims.

A research aim describes the main goal or the overarching purpose of your research project.

In doing so, it acts as a focal point for your research and provides your readers with clarity as to what your study is all about. Because of this, research aims are almost always located within its own subsection under the introduction section of a research document, regardless of whether it’s a thesis , a dissertation, or a research paper .

A research aim is usually formulated as a broad statement of the main goal of the research and can range in length from a single sentence to a short paragraph. Although the exact format may vary according to preference, they should all describe why your research is needed (i.e. the context), what it sets out to accomplish (the actual aim) and, briefly, how it intends to accomplish it (overview of your objectives).

To give an example, we have extracted the following research aim from a real PhD thesis:

Example of a Research Aim

The role of diametrical cup deformation as a factor to unsatisfactory implant performance has not been widely reported. The aim of this thesis was to gain an understanding of the diametrical deformation behaviour of acetabular cups and shells following impaction into the reamed acetabulum. The influence of a range of factors on deformation was investigated to ascertain if cup and shell deformation may be high enough to potentially contribute to early failure and high wear rates in metal-on-metal implants.

Note: Extracted with permission from thesis titled “T he Impact And Deformation Of Press-Fit Metal Acetabular Components ” produced by Dr H Hothi of previously Queen Mary University of London.

Research Objectives

Where a research aim specifies what your study will answer, research objectives specify how your study will answer it.

They divide your research aim into several smaller parts, each of which represents a key section of your research project. As a result, almost all research objectives take the form of a numbered list, with each item usually receiving its own chapter in a dissertation or thesis.

Following the example of the research aim shared above, here are it’s real research objectives as an example:

Example of a Research Objective

• Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.
• Investigate the number, velocity and position of impacts needed to insert a cup.
• Determine the relationship between the size of interference between the cup and cavity and deformation for different cup types.
• Investigate the influence of non-uniform cup support and varying the orientation of the component in the cavity on deformation.
• Examine the influence of errors during reaming of the acetabulum which introduce ovality to the cavity.
• Determine the relationship between changes in the geometry of the component and deformation for different cup designs.
• Develop three dimensional pelvis models with non-uniform bone material properties from a range of patients with varying bone quality.
• Use the key parameters that influence deformation, as identified in the foam models to determine the range of deformations that may occur clinically using the anatomic models and if these deformations are clinically significant.

It’s worth noting that researchers sometimes use research questions instead of research objectives, or in other cases both. From a high-level perspective, research questions and research objectives make the same statements, but just in different formats.

Taking the first three research objectives as an example, they can be restructured into research questions as follows:

Restructuring Research Objectives as Research Questions

• Can finite element models using simplified experimentally validated foam models to represent the acetabulum together with explicit dynamics be used to mimic mallet blows during cup/shell insertion?
• What is the number, velocity and position of impacts needed to insert a cup?
• What is the relationship between the size of interference between the cup and cavity and deformation for different cup types?

Difference Between Aims and Objectives

Hopefully the above explanations make clear the differences between aims and objectives, but to clarify:

• The research aim focus on what the research project is intended to achieve; research objectives focus on how the aim will be achieved.
• Research aims are relatively broad; research objectives are specific.
• Research aims focus on a project’s long-term outcomes; research objectives focus on its immediate, short-term outcomes.
• A research aim can be written in a single sentence or short paragraph; research objectives should be written as a numbered list.

How to Write Aims and Objectives

Before we discuss how to write a clear set of research aims and objectives, we should make it clear that there is no single way they must be written. Each researcher will approach their aims and objectives slightly differently, and often your supervisor will influence the formulation of yours on the basis of their own preferences.

Regardless, there are some basic principles that you should observe for good practice; these principles are described below.

Your aim should be made up of three parts that answer the below questions:

• Why is this research required?
• What is this research about?
• How are you going to do it?

The easiest way to achieve this would be to address each question in its own sentence, although it does not matter whether you combine them or write multiple sentences for each, the key is to address each one.

The first question, why , provides context to your research project, the second question, what , describes the aim of your research, and the last question, how , acts as an introduction to your objectives which will immediately follow.

Scroll through the image set below to see the ‘why, what and how’ associated with our research aim example.

Note: Your research aims need not be limited to one. Some individuals per to define one broad ‘overarching aim’ of a project and then adopt two or three specific research aims for their thesis or dissertation. Remember, however, that in order for your assessors to consider your research project complete, you will need to prove you have fulfilled all of the aims you set out to achieve. Therefore, while having more than one research aim is not necessarily disadvantageous, consider whether a single overarching one will do.

Research Objectives

Each of your research objectives should be SMART :

• Specific – is there any ambiguity in the action you are going to undertake, or is it focused and well-defined?
• Measurable – how will you measure progress and determine when you have achieved the action?
• Achievable – do you have the support, resources and facilities required to carry out the action?
• Relevant – is the action essential to the achievement of your research aim?
• Timebound – can you realistically complete the action in the available time alongside your other research tasks?

In addition to being SMART, your research objectives should start with a verb that helps communicate your intent. Common research verbs include:

Table of Research Verbs to Use in Aims and Objectives

Last, format your objectives into a numbered list. This is because when you write your thesis or dissertation, you will at times need to make reference to a specific research objective; structuring your research objectives in a numbered list will provide a clear way of doing this.

To bring all this together, let’s compare the first research objective in the previous example with the above guidance:

Checking Research Objective Example Against Recommended Approach

Research Objective:

1. Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.

Checking Against Recommended Approach:

Q: Is it specific? A: Yes, it is clear what the student intends to do (produce a finite element model), why they intend to do it (mimic cup/shell blows) and their parameters have been well-defined ( using simplified experimentally validated foam models to represent the acetabulum ).

Q: Is it measurable? A: Yes, it is clear that the research objective will be achieved once the finite element model is complete.

Q: Is it achievable? A: Yes, provided the student has access to a computer lab, modelling software and laboratory data.

Q: Is it relevant? A: Yes, mimicking impacts to a cup/shell is fundamental to the overall aim of understanding how they deform when impacted upon.

Q: Is it timebound? A: Yes, it is possible to create a limited-scope finite element model in a relatively short time, especially if you already have experience in modelling.

Q: Does it start with a verb? A: Yes, it starts with ‘develop’, which makes the intent of the objective immediately clear.

Q: Is it a numbered list? A: Yes, it is the first research objective in a list of eight.

Mistakes in Writing Research Aims and Objectives

1. making your research aim too broad.

Having a research aim too broad becomes very difficult to achieve. Normally, this occurs when a student develops their research aim before they have a good understanding of what they want to research. Remember that at the end of your project and during your viva defence , you will have to prove that you have achieved your research aims; if they are too broad, this will be an almost impossible task. In the early stages of your research project, your priority should be to narrow your study to a specific area. A good way to do this is to take the time to study existing literature, question their current approaches, findings and limitations, and consider whether there are any recurring gaps that could be investigated .

Note: Achieving a set of aims does not necessarily mean proving or disproving a theory or hypothesis, even if your research aim was to, but having done enough work to provide a useful and original insight into the principles that underlie your research aim.

2. Making Your Research Objectives Too Ambitious

Be realistic about what you can achieve in the time you have available. It is natural to want to set ambitious research objectives that require sophisticated data collection and analysis, but only completing this with six months before the end of your PhD registration period is not a worthwhile trade-off.

3. Formulating Repetitive Research Objectives

Each research objective should have its own purpose and distinct measurable outcome. To this effect, a common mistake is to form research objectives which have large amounts of overlap. This makes it difficult to determine when an objective is truly complete, and also presents challenges in estimating the duration of objectives when creating your project timeline. It also makes it difficult to structure your thesis into unique chapters, making it more challenging for you to write and for your audience to read.

Fortunately, this oversight can be easily avoided by using SMART objectives.

Hopefully, you now have a good idea of how to create an effective set of aims and objectives for your research project, whether it be a thesis, dissertation or research paper. While it may be tempting to dive directly into your research, spending time on getting your aims and objectives right will give your research clear direction. This won’t only reduce the likelihood of problems arising later down the line, but will also lead to a more thorough and coherent research project.

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SMART Goals and Objectives: Definition, Characteristics, and Examples

Setting SMART objectives and goals is an important step toward success in both personal and professional life. However, merely stating goals or purpose is insufficient; it must be SMART goal. SMART objectives and goals assist you in developing objectives that are clear, specified, measurable, achievable, relevant, and time-bound. 

In this article, we’ll define SMART objectives and goals, describe their qualities, and present examples to help you understand how to use them.

Content Index

What are SMART goals and objectives?

What does s.m.a.r.t. stand for, why should you clearly define smart goals and objectives, management by objectives (mbo).

• Principles in setting up SMART Goals and Objectives
• What is the Difference between Smart Goals and Objectives?
• Importance of SMART Goals and Objectives
• Advantages and disadvantage of SMART goals and objectives
• SMART Objectives and Goals Examples

SMART goals and objectives are a method for establishing Specific, Measurable, Achievable, Relevant, and Time-bound aims. The SMART framework defines goals and objectives clearly and practically, making them more actionable and increasing the likelihood of success. SMART is an acronym that stands for SMART goal and is used to help in goal setting. 

In this modern, technology-driven world, one of the most widely used words is “SMART.”

This word is utilized in many industries due to its efficiency and objectivity. The SMART technique is also a practical tool that can save relevant professionals in competitive industries like marketing, sales, advertising, market research, etc. 

Smartphones, smart TVs, and other everyday items have this word prefixed to their names. We now realize that term refers to something intelligent due to its operation and technological progress.

More than just Theory: 300+ Ready-Made Survey Templates to evaluate your SMART goal and objective.

Take a few minutes to clear your head; let us analyze the SMART method to achieve SMART objectives and goals. With constant practice, it will be easier to apply this method. However, for starters, let us understand what each alphabet in the word “SMART” mean.

M-MEASURABLE

A-achievable, t-time-based.

Do you know what the importance of clearly defining objectives and goals is?

• Time doesn’t pass in vain for anyone, more importantly, not for organizations or businesses. Every minute, every second, a new idea is conceptualized, and with these ideas growing, there is a growing competition out there.
• Every day there is a new organization or business ready to give tough competition to its counterparts and competitors.  In this competitive atmosphere, it is also essential to win customers and also understand customer satisfaction levels. Not only this, you have to constantly monitor to verify that every department in your business or organization is working efficiently, just like perfect machinery. 
• It may sound like a tedious process in which one question leads you to more questions , and then it seems like a never-ending story because only some know how to land their thoughts. Remember, putting down your goals and objectives on paper will help you put your thoughts and your imagination to work in reality.

To summarize it in a concise and very significant sentence: walking without objectives is like navigating without a compass. 

Imagine the immensity of the open sea and you in the middle of it, it is a moment in which you do not know what to do, nor do you know the resources you can count on and much less know which side of the ocean or sea will be better to go.

The best thing is to start making some kind of effort to move forward, right? You cannot stay there; however, it is difficult to know at that stage if everything you do will have optimal results and bring you closer to the right path. 

The most likely thing is that these efforts might exhaust you, and you do not know if everything you did will be worthwhile for something. On the contrary, if you know the goal you should reach, it will be easier to use your energy to achieve it once and for all.

People, groups, and systems need clear, structured, and well-defined objectives from the particular to the general. setting a goal is stated to gain a clear understanding of what needs to be delivered, and the person assessing may then judge the outcome based on defined smart criteria.

The same happens with the objectives of a company. We all have an end to this life, and we cannot get up every day thinking about facing when we are approaching the end because, in this way, there will come a time when we feel that we are not doing enough to sustain ourselves in this world.

Learn more: Demographic Segmentation .

SMART objectives are a primary way to collect feedback and communicate within the organization. SMART goal and objective is directly derived from management by objectives (M.B.O.). It was an effective way of completing tasks by prioritizing objectives.

Feedback is important because it showcases the room for improvement and is an insight into the company. Feedback includes periodic checks to measure current results vs. expected and current results vs. end objectives. The progress can be recorded by asking basic questions like:

• Is the plan being executed in the right manner?
• Are the efforts tangible that they are aiding the progress of the project?
• Are changes required to be made to the current plan?

The SMART objective helps break down these questions and goals even further, where the scope of every milestone is measured. The SMART objective help set goals and track progress to meet the end objective. 

A SMART goal helps in following through on goals and prevents getting distracted. Through these goals, different objective goals can be set up, namely:

Long term goals

Intermediate-term goals, short term goals, principles in setting up smart goals and objectives.

It is an essential task to write SMART goals and objectives and setting up them. The smart goal criteria or the principles of goal-setting theory are:

Task complexity

What is the difference between smart goals and smart objectives.

“SMART goals” and “SMART objectives” are frequently used interchangeably, although their meanings might vary depending on the context. However, in some circumstances, a difference can be made between the two. Let us analyze the distinction:

• Goals are broad, all-encompassing statements that describe the desired outcome or result. They are frequently long-term and give a general framework for your activities.
• These goals are clear, measurable, doable, important, and have deadlines. They use the SMART goal framework to ensure their goals are clear and improve their chances of success.
• Goals concentrate on the “what” you want to accomplish and provide a precise aim to work toward.

SMART Objective

• Objectives are more defined, short-term milestones that help to reach the overall aim. They are actionable steps that explain the actions and activities needed to achieve the intended result.
• SMART objectives are specific, measurable, achievable, relevant, and time-bound. They are intended to be more specific and to provide a clear roadmap for attaining the goal.
• SMART Objectives concentrate on the “how” and “when” of achieving a goal, breaking it into achievable steps.

Importance of SMART goals and objectives

SMART goals and objectives are highly important for individuals and organizations alike. Here are several reasons why they are crucial:

Clarifies end objective

Effective time management, reminds you of priorities, obliges to take action, advantages and disadvantages of smart goals and objectives.

SMART goals and objectives offer several advantages, but they also have some potential disadvantages. Let’s explore both sides:

Advantages of SMART goals and objectives

SMART goals and objectives have several advantages that make them effective. Some of the primary advantages are as follows:

They are not vague: Since SMART goals and objectives are extremely procedural, each milestone and feedback is planned and monitored in complete detail. It mitigates the factor of uncertainty.

Missed work is easy to track: Each person is given a specific responsibility; hence, when work is not completed, it is very easy to troubleshoot the gaps in delivery. It makes everyone extremely accountable, and any loss of work is easy to track.

Goals are divided into small achievable objectives: SMART goals have an end, but SMART objectives are further divided into bite-sized milestones. Hence, no matter the scale of the end goal, it is very easily achievable.

Disadvantages of SMART goals and objectives

SMART goals and objectives have several disadvantages that make them effective. Some of the primary disadvantages are as follows:

No importance to other tasks: All other work gets ignored due to the system’s rigidity. Also, there is lesser scope for innovation or trying to complete work differently because the work is milestone based.

Lots of pressure: There is immense pressure to complete work in a given time frame, making the environment extremely stressful and challenging.

Different interpretations by different people: The pressure to complete goals and objectives is open to interpretation by different people. The urgency or rigidity of the process is construed differently by different people.

LEARN ABOUT: Theoretical Research

SMART objectives and goals examples

Here are a few examples to help you strategize and define your organization’s SMART objectives and Goals :

• Defining objectives requires time, patience, and the complete know-how of how an organization functions, but it needs clarity above anything else. For example, many organizations need to define clear objectives, which reflects in many ways. But with SMART objectives and methods, it is possible to define goals clearly.
• To clearly define objectives, one may need to sit down and ask questions, for example, “What is that I would like my organization or business to achieve? Why do I want to achieve these targets? Do I have the necessary resources to achieve these objectives?”, among many other questions that you need to ask yourself, let’s accept it feel like this is boring and absolutely unnecessary.  
• Take a step back and think, do you even know how many companies have suffered bankruptcy because they didn’t feel the need to define their objectives? You might even be tempted to think that the process is a complete waste of your time. Instead of wasting your valuable time speculating and penning down your thoughts, it is advisable to get down to do some real work and start with the action plan.
• While you may have decided to start functioning without a plan, there are hundreds of companies that take the target date to do it step by step and then achieve goals in a faster way, winning customers, reducing customer churn , taking away place, and even unseat everything you have already achieved. Taking action concretely and clearly understanding facts and figures will not take you anywhere. On the contrary, this will waste your time, effort, work, quality, and even your reputation in the market.
• To avoid all the tragedies previously reported, do you still think this is a waste of time? Do you still believe that it is better to postpone it? If your answer is NO, congratulations! The SMART method is easier than you imagined it to be.  

Learn more: Customer Satisfaction Surveys

Let us take an example to understand how SMART objectives and SMART goals help save time. Image an organization that works to remove plastic bags and similar waste from the entire city has objectives and goals defined as:

“Our goal is to make the entire city clean and free of any plastic and plastic waste.”

This goal is a little vague. However, if the objective and goal were rewritten as, “As an organization, we aim to clean the city and make it free from any plastic waste in the next two years with the help and support of our volunteers.”

The second time the goal and objective were rewritten, it had a particular timeline, the specific activity was mentioned, who would be helping the organization was clear, and what they wanted to achieve was certain.

In this manner, people who are associated with the organization know what their tasks are and what the time-bound in which they need to achieve them. This helps avoid any confusion, and activities go on smoothly without hesitation.

LEARN ABOUT: Behavioral Targeting

SMART objectives and Goals are an important part of a company’s growth. The Managers and Directors of Marketing, Sales, Human Resources, and many other areas must be fully involved in defining these goals.

For all, the growth of the company also implies personal growth. The only way to achieve this is by having order and structure clearly defining the objectives.

Do not waste more time doing actions that won’t yield the desired results. Start defining your SMART objectives and give your team enough reasons why they should get down to work as soon as possible. 

Giving them a good goal is part of the motivation everyone in the organization needs. Remember increasing team productivity is always favorable and does wonders for achieving the organization’s overall growth.

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Objectives and goals: writing meaningful goals and smart objectives.

The content on this page follows the half-hour on-demand webinar: Writing meaningful goals and SMART objectives

• A goal is an aspirational statement about what you want to achieve
• An objective describes how you'll show progress toward your goal

Meaningful goals SMART objectives Further resources

Meaningful goals

What is a goal.

A goal is an aspirational statement about what you want to achieve:

• Broad, future-oriented statement that describes expected effect
• Defines scope
• Provides framework for objectives

Example: All employees in Tubman County work in environments that support mental well-being.

Checklist: What makes a meaningful goal?

• Free from jargon
• Specific about expected effect
• Easily understood
• Declarative statement
• Does not include solution or specific service/program
• Conveys ultimate destination

Considering equity in writing goals

Many factors drive an organization's goals, like codes and statutes, funders, accreditation, and other plans. However: When setting goals, starting with an organization's needs might not be the right place to start, even though it's often the easiest or most intuitive.

We can't completely disregard forces like funders and and statutes, of course, but they also can't be the only thing that we consider when writing goals. Our commitment to health equity requires us to ask some questions when we're writing goals, especially as part of a community health improvement plan:

• Whose goals and standards are these?
• What do our goals and standards say about who and what we value?
• Who is at the table? Who is missing?
• Do the questions we're asking matter to those most impacted?
• Are we aiming upstream?
• What systems change is possible?
• How are we considering racism?

Source: Michigan Public Health Institute

SMART objectives

A SMART objective is one that is specific, measurable, achievable, relevant, and time-bound. SMART objectives provide the details for how a group or organization will achieve a goal.

In order to understand how the parts of SMART objectives flow together, the order of the SMART components listed below will go out of order— SMTRA . This is because the Specific, Measurable and Time-Bound parts are clearly visible in the standard written format for objectives. The Achievable and Relevant pieces are more abstract and require reflection. Each of these parts will include an example objective that will be re-written to be SMART.

SMART objectives should:

• Include all components of SMART
• Relate to a single result
• Be clearly written (use plain language, avoid jargon)

Specific objectives:

• Are precise
• Are clear to team, partners, and other groups
• Use plain language and avoid jargon
• Use verbs that document action

Prompts to consider when writing specific objectives include:

• Who : Who will be impacted? Who is your focus population?
• What : What do you intend to impact?

Note that not all of these questions will apply to every objective.

Example: Reduce the percent of Tubman County  students in grades 6 through 12 who have smoked cigarettes in the past 30 days .

How will we show impact over time? Use a measure , to show progress toward a target :

• Measure : A measure is a number, percent, or standard unit used as a reference point from which change can be monitored.
• Target : A target is the direction we want to move the measure, or the level we want to reach.
• Data source : Be sure to tie your measure and target to a specific data source, like a regular survey or publication, or a state or local agency.

Prompts to consider when writing measurable objectives include:

• How much and in what direction will change occur?
• What data will you use to measure? 
• Where will this data come from?
• Is there a stand-in or proxy measure to use if you cannot directly measure this objective? If not, would another measure be more appropriate instead?

Example: Decrease by 5 percentage points the number of Tubman County students in grades 6 through 12 who have smoked cigarettes in the past 30 days ( baseline: 18%; data source: 2019 Minnesota Student Survey ).

Time-bound objectives attach a reasonable date by which and objective will happen.

• Not too soon: Give enough time to demonstrate success and/or the connection between action and outcome
• Not too far away: Don't encourage procrastination, or remove the ability to connect the dots between action and outcome
• Consider when data will be available: May determine your time for you

Prompts to consider when writing time-bound objectives include:

• Is this time frame realistic?
• Should it be closer? Should it be further away?
• When will the data be available?

Example: By December 31, 2022 , decrease by 5 percentage points the number of Tubman County students in grades 6 through 12 who have smoked cigarettes in the past 30 days (baseline: 18%; data source: 2019 Minnesota Student Survey).

Objectives should be within reach for your partners, community, or team, and consider available resources, knowledge, and time. Remember, considering what's achievable for your team or organization often requires thought and discussion.

Prompts to consider when writing achievable objectives include:

• How will the group accomplish this objective?
• Does the current time frame or environment help or hinder this objective? Should we scale the target or time frame up or down?
• What resources will help us achieve this objective? What limitations or constraints stand in our way?

A note of caution about setting objectives for long-term, population-level change:

• Complex, long-term issues require decades of work for change; your organization's actions are one small part
• Who's on the hook if you don't achieve your target?
• Is it more appropriate to measure movement direction without setting a concrete target number? (e.g., increase, decrease, or maintain)
• Consider intermediate objectives when appropriate

Example intermediate objective with target direction AND number: By December 31, 2022, increase the percent of establishments that pass tobacco compliance checks from 75% to 80% (data source: 2020 Tubman County Sheriff's Department).

Example long-term objective with JUST target direction: By December 31, 2025, decrease the percent of Tubman County students in grades 6-12 who smoked cigarettes in the past 30 days (baseline: 82%, data source: 2019 Minnesota Student Survey).

Relevant objectives align with a corresponding goal and with an organization or group's mission, vision, and values. They're important to partners, community members, and decision-makers, and they help achieve meaningful change for focus populations.

Prompts to consider when writing relevant objectives include:

• Will objective contribute to achieving goal? 
• Is it worthwhile and meaningful to measure this objective?

Different ways to write SMART objectives

There are multiple approaches and ways to explain how to write SMART objectives. Here are some other sentence structures for objectives:

[ Who ] will do [ what ] resulting in [ measure ] by [ when ].

By [ when ], [ who ] will do [ what ] resulting in [ measure ].

By [ when ], [ measure - includes who and what ].

[ Measure – includes who and what ] by [ when ].

Further resources

Writing meaningful goals and SMART objectives Minnesota Dept. of Health

Writing SMART Objectives (PDF) Centers for Disease Control and Prevention

CHIP Collaborative Handbook: Community Health Improvement Planning (PDF) Kansas Health Institute

"SMART" Objectives (PDF) March of Dimes, Hawaii Chapter

Developing Goals, Objectives, and Performance Indicators for Community Health Improvement Plans (PDF) National Association of County & City Health Officials (NACCHO)

Developing and Using SMART Objectives Public Health Quality Improvement Exchange (PHQIX)

• public health practice

Mastering the SMART Goal: A Guide to Achieving Your Objectives

by Michael Rucker | Apr 1, 2007 | Entrepreneurship | 0 comments

Developing a SMART goal is essential for ensuring your objectives are focused and measurable. This strategy not only clarifies your goals but also incorporates a quantitative element to track progress effectively. Understanding and applying the SMART goal framework can significantly enhance your ability to achieve desired outcomes.

What is a SMART Goal?

Definition: The SMART acronym stands for Specific, Measurable, Attainable, Relevant, and Time-bound. Each component plays a crucial role in crafting effective goals:

• Specific: A goal must be clear, detailed, and well-defined, leaving no ambiguity about what is to be achieved.
• Measurable: It should have quantifiable metrics to track progress and success.
• Attainable: The goal must be realistic and achievable with the resources and skills available.
• Relevant: Ensure the goal is pertinent to your desires and current situation, avoiding unrealistic expectations.
• Time-bound: Establishing a deadline motivates progress, sets milestones for early wins, and facilitates progress evaluation.

Examples of a SMART Goal

• “I aim to lose 16lbs, reducing my body mass index from 27 to 24 by November 30, 2025.”
• “I commit to completing 200 pages of my dissertation by August 15, 2025.”

Creating a SMART Goal

Steps for formation:.

• Identify Your Objective: Clearly define what you wish to accomplish, considering your strengths and limitations.
• Set a Realistic Deadline: Choose a timeframe that is challenging yet achievable.
• Specify Your Goal: Write it down in a concise sentence, avoiding vagueness. Utilize action verbs to ensure clarity and measurability.
• Evaluate Realism and Challenge: Ensure your goal is ambitious enough to motivate you but not so high as to set you up for failure.

Action Verbs to Clarify Goals:

• Demonstrate

SMART Goal Hints and Tips

• Refinement is Key: Be as specific as possible with every aspect of your goal.
• Simplicity and Planning: Determine the investments of time, money, and effort required.
• Commit to Timelines: Specify the completion date with exact days, months, and years.

To refine your goals, ask yourself:

• What challenges might arise, and how can you overcome them?
• What knowledge or skills are needed to achieve the goal?
• What benefits will achieving this goal bring?

Patience and deliberate planning, coupled with resolve, are your allies in goal achievement.

Extending to SMARTER Goals

• Ethical (E): Align your goals with your moral values.
• Recorded (R): Document your goal and track your progress meticulously.

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Defining the “Smart Hospital”: A Literature Review

• Conference paper
• First Online: 13 May 2024
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• Leonidas Anthopoulos   ORCID: orcid.org/0000-0002-7731-4716 14 ,
• Maria Karakidi 14 &
• Dimitrios Tselios   ORCID: orcid.org/0000-0002-5840-5858 14  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 986))

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• World Conference on Information Systems and Technologies

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Smart hospital grassroots date back to the late 20s when the Information and Communication Technologies (ICT) started being adopted by the health sector to digitize its administrative processes. These technologies reduced mistakes, minimized the operational costs, and enhanced the efficiency of hospital management. The primary goal of the ICT utilization is to optimize healthcare delivery using modern data and technologies, improving safety, satisfaction, patient empowerment, clinical outcomes, and performance. Emerging ICT like big data analytics, artificial intelligence, IoT, cloud computing, 5G networks, and blockchain have enabled the integration and interoperability of various data sources and systems, enhancing the efficiency and quality of healthcare services. Moreover, these technologies introduced the terms smart health and smart hospital. The aim of this work in progress is to define smart hospital, explain its context and services, and distinguish it from other emerging approaches (agile, hybrid and green hospital) according to the literature review findings.

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Anthopoulos, L.G.: Understanding Smart Cities: A Tool for Smart Government or an Industrial Trick? Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57015-0

Book   Google Scholar  

PRISMA Homepage. https://www.prisma-statement.org . Accessed 21 Nov 2023

Cochrane, C., Hertleer, C., Schwarz-Pfeiffer, A.: Smart textiles in health. In: Smart textiles and their applications, pp. 9–32. Elsevier (2016). https://doi.org/10.1016/B978-0-08-100574-3.00002-3

Chapter   Google Scholar  

Tian, S., Yang, W., Le Grange, J.M., Wang, P., Huang, W., Ye, Z.: Smart healthcare: making medical care more intelligent. Global Health J. 3 (3), 62–65 (2019)

Article   Google Scholar  

Malik, H., Iqbal, A., Joshi, P., Agrawal, S., Bakhsh, F.I. (eds.): Metaheuristic and evolutionary computation: algorithms and applications. SCI, vol. 916. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-7571-6

Ramson, S.J., Vishnu, S., Shanmugam, M.: Applications of internet of things (IoT)–an overview. In: IEEE 2020 5th International Conference on Devices, Circuits and Systems (ICDCS), pp. 92–95 (2020)

Google Scholar  

Reddy, S., Allan, S., Coghlan, S., Cooper, P.: A governance model for the application of AI in health care. J. Am. Med. Inform. Assoc. 27 (3), 491–497 (2020)

Hassan, A., Prasad, D., Khurana, M., Lilhore, U.K., Simaiya, S.: Integration of internet of things (IoT) in health care industry: an overview of benefits, challenges, and applications. In: Taneja, K., Taneja, H., Kumar, K., Selwal, A., Ouh, E.L. (eds.) Data Science and Innovations for Intelligent Systems: Computational Excellence and Society 5.0, 1st edn. CRC Press (2021). https://doi.org/10.1201/9781003132080

Bouri, N., Ravi, S.: Going mobile: how mobile personal health records can improve health care during emergencies. JMIR Mhealth Uhealth 2 (1), e3017 (2014)

Zhou, L., DeAlmeida, D., Parmanto, B.: Applying a user-centered approach to building a mobile personal health record app: development and usability study. JMIR Mhealth Uhealth 7 (7), e13194 (2019)

Park, Y.R., et al.: Managing patient-generated health data through mobile personal health records: analysis of usage data. JMIR mHealth uHealth 6 (4), e9620 (2018)

Etemadi, S., Mohammadi, B., Akbarian Bafghi, M.J., Hedayati Poor, M., Gholamhoseini, M.T.: A new costing system in hospital management: time-driven activity based costing: a narrative review. Evid. Based Health Policy Manage. Econ. 2 (2), 133–140 (2018)

Sohn, L.B.: Stockholm declaration on the human environment. Harvard Int. Law J. 14 (3), 423–515 (1973)

Wagenaar, C. (ed.): The Architecture of Hospitals. NAI Publishers, Rotterdam (2006)

Anesi, G. L., Lynch, Y., Evans, L.: A conceptual and adaptable approach to hospital preparedness for acute surge events due to emerging infectious diseases. Crit. Care Explor. 2 (4), e0110 (2020)

Holzinger, A., Röcker, C., Ziefle, M.: From smart health to smart hospitals. Smart Health: Open Problems and Future Challenges, 1–20 (2015)

Lennox, L., Linwood-Amor, A., Maher, L., Reed, J.: Making change last? Exploring the value of sustainability approaches in healthcare: a scoping review. Health Res. Pol. Syst. 18 (1), 1–24 (2020)

Nasr, M., Islam, M.M., Shehata, S., Karray, F., Quintana, Y.: Smart healthcare in the age of AI: recent advances, challenges, and future prospects. IEEE Access 9 , 145248–145270 (2021)

Ndayishimiye, C., et al.: Associations between the COVID-19 pandemic and hospital infrastructure adaptation and planning—a scoping review. Int. J. Environ. Res. Public Health 19 (13), 8195 (2022)

Papadopoulos, A.M.: Energy efficiency in hospitals: Historical development, trends and perspectives. Energy Performance of Buildings: Energy Efficiency and Built Environment in Temperate Climates, 217–233 (2015)

Prada, M., et al.: New solutions to reduce greenhouse gas emissions through energy efficiency of buildings of special importance–hospitals. Sci. Total. Environ. 718 , 137446 (2020)

Stevanovic, M., Allacker, K., Vermeulen, S.: Hospital building sustainability: the experience in using qualitative tools and steps towards the life cycle approach. Procedia Environ. Sci. 38 , 445–451 (2017)

Vergunst, F., Berry, H.L., Rugkåsa, J., Burns, T., Molodynski, A., Maughan, D.L.: Applying the triple bottom line of sustainability to healthcare research—a feasibility study. Int. J. Qual. Health Care 32 (1), 48–53 (2020)

Zhao, W., Luo, X., Qiu, T.: Smart healthcare. Appl. Sci. 7 (11), 1176 (2017)

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Acknowledgements

Parts of this article are based on a MSc thesis at the MSc in Agile Management Methods, University of Thessaly, Greece.

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Leonidas Anthopoulos, Maria Karakidi & Dimitrios Tselios

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Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

Gintautas Dzemyda

DCT, Universidade Portucalense, Porto, Portugal

Fernando Moreira

Institute of Information Technology, Lodz University of Technology, Łódz, Poland

Aneta Poniszewska-Marańda

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Anthopoulos, L., Karakidi, M., Tselios, D. (2024). Defining the “Smart Hospital”: A Literature Review. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Poniszewska-Marańda, A. (eds) Good Practices and New Perspectives in Information Systems and Technologies. WorldCIST 2024. Lecture Notes in Networks and Systems, vol 986. Springer, Cham. https://doi.org/10.1007/978-3-031-60218-4_15

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Home > Books > Marketing - Annual Volume 2024 [Working Title]

Internet of Marketing Things: A Fog Computing Paradigm for Marketing Research

Submitted: 06 September 2023 Reviewed: 20 February 2024 Published: 17 May 2024

DOI: 10.5772/intechopen.114333

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Conventional market research is usually costly, time-consuming, scalability issue, and intrusive, and the generated data may have a short shelf life in fast-moving markets. The latest effort in delivering computing resources as a service to marketing researchers and managers represents a change from computing as an over-the-counter service that is obtained to computing as a service that is provided to users online, over the internet from very large databases. Managing the data and research produced by internet of things (IoT) devices, such as actuators and sensors, is a major issue faced by marketing research and executives when using an IoT system. This paper demonstrates how commonly used cloud-based IoT systems are challenged by the heterogeneity, large amount, and high latency shown in some cloud marketing ecosystems. We introduce academia and managers to a recent major development, “Fog Computing,” a transpiring computational framework that decentralizes strategies, applications, and data analysis into the network itself using a federated and distributed computing system. It converts centralized cloud to distributed fog by bringing computation and storage near the end user. Fog computing is regarded as a novel market paradigm which can assist artificial intelligence and marketing research and strategies, specifically for the architecture of more advanced research systems.

• fog computing
• cloud computing
• recommender system
• internet-of-things (IoT)
• marketing analytics

Author Information

Jacob hornik *.

• Coller School of Management, Tel-Aviv University, Israel

Matti Rachamim

• Graduate School of Business Administration, Bar-Ilan University, Israel

*Address all correspondence to: [email protected]

1. Introduction

“With the emergent of fog computing, the era of the cloud’s total dominance is drawing to a close” [ 1 ].

Computing is regarded as a critical driving force in the development of human systems. Technological advances like cloud computing and measurement devices over the past few years have provided firms, researchers, policy makers, and access to individual-level data of unprecedented amount. The development of the Internet of Things (IoT), artificial intelligence (AI), the adoption of 5G, and advances toward 6G technology has led to the aggregation of very large scale of marketing data, but a significant portion of the data delivered by smart ecosystems, like smart/intelligent marketing lasts unused [ 2 , 3 ]. In fact, the commonly used cloud-based IoT ecosystems are challenged by heterogeneity, high latency, and large scale, observed in many cloud systems. Thus, ubiquitous use of smart, interconnected devices is estimated to reach 58.2 billion units by 2025 [ 4 ]. This huge expansion is fueled by the increase of mobile devices such as laptops, tablets, and mobile phones, with smart sensors serving a variety of markets and industries, such as smart cities, autonomous transportation, smart homes, industrial controls, smart power grids, wireless sensors and actuators networks wearables, and smart marketing [ 5 ]. New technologies and concepts are required to manage this growing amount of IoT devices and services producing very large scales of “big-data.” Indeed, big-data is an important aspect of marketing research (e.g., [ 6 , 7 , 8 ]). Evidently, relying on common systems like cloud computing is not capable of facing the requirements of large data management [ 9 ]. Therefore, a fog computing (FC) algorithm was introduced to reduce the processing load from the cloud by delegating some of the tasks to the fog ( Table 1 ).

Summary of comparison between cloud and fog computing.

1.1 Study objectives

Inspired by recent successful applications of FC to various smart ecosystems, and the clear gap in marketing research, in the present study, we suggest future applications to smart marketing ecosystem. To advance these objectives, we first provide an overview of FC and related concepts relevant to marketing research. Second, we elaborate on intelligent/smart marketing. Third, we present some illustrations of FC marketing research applications. Fourth and finally, we identify a number of fundamental research challenges for marketing in order to fully exploit FC for marketing management and research. This conceptual study goes beyond a literature review by offering compelling observations of marketing in the real world. Employing a multiperspective approach, it aims to deliver valuable insights into FC offering perspective on several impacted marketing research areas.

Inspired by many important applications of FC to various smart ecosystems, we outline future important application to marketing research. To advance these, we start with a short review of FC and related concepts relevant to marketing research. Second, we elaborate on FC-enabled marketing research.

2. Fog computing

Fog computing, also known as fog networking or fogging, is a decentralized computing infrastructure that extends cloud computing to the edge of the system. It allows data, applications, and other resources to be located closer to the end users. This approach aims to address the limitations of traditional cloud computing, such as latency, bandwidth constraints, and security concerns, by bringing computation and data storage closer to the devices where it is needed. Fog computing is distributed across multiple devices and nodes at the edge of the network, rather than relying on a centralized cloud infrastructure. The data processing and analysis occur closer to the data source, reducing latency and improving response times. FC nodes can gather and analyze data from local sources, enabling real-time decision-making based on local context. It provides security by reducing the amount of sensitive data transmitted over public networks. FC enables real-time analysis of data, allowing for immediate responses and actions. IT can be easily scaled to accommodate increasing data volumes and processing demands [ 6 , 10 ]. FC architecture ( Figure 1 ) consists of highly heterogeneous dispersed end devices with the aim of enabling deployment of IoT devices that requires computation, storage, and resources distributed at different locations [ 4 , 11 ]. Figure 1 exhibits FC in the larger context of a cloud-based ecosystem serving smart end-devices. Thus, one can regard FC as bridge between the cloud and the edge of the network which facilitates the deployment of the newly emerging IoT marketing ecosystems. For marketing research, it is vital to outline the major component of the FC architecture – the fog nodes.

FC architecture.

2.1 Fog nodes

Fog nodes are the backbone of FC, providing a distributed and scalable computing infrastructure that extends cloud capabilities to the edge of the network. They enable real-time data processing, reduced latency, improved bandwidth utilization, and enhanced security, making them essential for a wide range of applications in the IoT and other latency-sensitive domains. Fog nodes can be physical devices (e.g., switches, routers, gateways, etc.) or virtual devices (e.g., virtualized switches, virtual machines, etc.) that are firmly connected to the end components and present computing qualifications to the end components [ 4 ]. FC nodes are responsible for processing, storing, and communicating data between the cloud and edge devices. Fog nodes can be any type of device, such as a computer, server, smartphone, or even a sensor. They are typically located close to the data source in order to reduce latency and improve performance. Thus, they play a crucial role in processing and managing data locally. Fog nodes can be categorized into main types: Fog devices : these are the simplest type of fog node and are typically used to collect and transmit data. They are the edge devices that directly interact with the physical world and generate data. Examples include sensors, actuators, cameras, and other IoT devices. Edge devices are often the entry points for data into the fog computing infrastructure. Fog gateways : these are fog nodes that connect fog devices and fog servers to the cloud. They are responsible for routing data between the different layers of the fog computing network. These are intermediary devices that aggregate and process data from edge devices before transmitting relevant information to the cloud or other parts of the network. Fog nodes can have more computational power than edge devices and may perform tasks like data filtering, preprocessing, and some level of analysis. Smartphones and tablets: personal mobile devices can also act as fog computing nodes, especially in scenarios where they are used to process local data or contribute to distributed. Network equipment : networking components such as routers and switches can be considered as fog computing nodes, especially if they have built-in computing capabilities. These devices can assist in routing data efficiently and may contribute to local processing. Vehicles: in the context of applications like autonomous driving, vehicles themselves can be considered fog computing nodes. They process data from onboard sensors and make local decisions to ensure the safety and efficiency of the vehicle. Thus, fog nodes typically use a variety of technologies to communicate with each other, including Wi-Fi, Bluetooth, and Ethernet. They also use a variety of protocols to exchange data, such as HTTP. The specific type of fog node that is used for a particular application depends on the specific requirements of the application. For example, a fog device might be used to collect data from a sensor, while a fog server might be used to process and analyze that data. FC nodes are a key component of fog computing architecture. In sum, fog nodes provide the processing, storage, and communication capabilities that are essential for fog computing applications. The following deployment models important for marketing research are also supported [ 12 ].

Private fog node : a fog node intended for exclusive use by a single organization comprising multiple users (e.g., business units).

Community fog node : a fog node that is for use only by a unique groups of users from organizations that have shared objectives (e.g., police, security).

Public fog node : a fog node intended for open use by the general public. It exists on the grounds of the fog server.

Hybrid fog node : a unified fog node that consists of many different nodes (private, community, or public) that are defined entities, but are connected by common or proprietary technologies that enables data and application portability.

3. SDN-based fog computing

SDN-based FC integrates Software-Defined Networking (SDN) with FC to address the challenges of managing and orchestrating a large-scale fog network. SDN provides a centralized and programmable control plane for the fog network, enabling efficient resource management, dynamic traffic optimization, and flexible network programmability. SDN is a networking approach that separates the control plane from the data plane, allowing for more centralized and flexible network management [ 11 ]. It provides independent features and protocols, overcoming the issues related to the closed hardware and proprietary software. By combining SDN and FC, SDN-based fog computing offers a number of advantages, including the work by [ 13 ].

Reduced latency : fog computing brings computing and storage resources closer to the edge of the network, where data are generated and consumed. This can help to reduce latency for applications that are sensitive to delay, such as real-time video streaming and augmented reality.

Improved scalability : SDN can help to scale fog computing networks more efficiently by providing centralized control over network resources. This can be especially beneficial for applications that require a large number of fog nodes, such as smart cities and the Internet of Things (IoT).

Increased flexibility : SDN makes it easier to deploy and manage fog computing networks. For example, SDN can be used to create virtual networks that are tailored to the specific needs of different applications.

Enhanced security : SDN can help to improve the security of fog computing networks by providing centralized control over network access and traffic routing. This can help to prevent unauthorized access to network resources and protect against cyberattacks.

SDN unlike the traditional network, and, as shown in Figure 2 , it divides the control plane from the data plane. The system provides with the possibility to optimize service operations as well as other service demands from a centralized “User Interface” (UI), providing added programmability, agility, and the capability to apply network automation [ 14 ]. The separation of the control and data planes provides some important advantages: (1) it can split the vertical integration, and (2) it simplifies policy enforcement and network (re)configuration and evolution. Figure 2 shows the three-plane SDN-based IoT architecture.

DSN-enabled fog computing.

The SDN controller and the network devices are connected with each other by the southbound API. Application plane can be of diverse FC applications as smart marketing. The application and control planes communicate with each other using the northbound API. The FC has an increasing number of end devices, and software like SDN help to provide quality of service.

4. FC applications

FC is frequently updated, and the number of applications deployed in a fog technology is growing [ 5 , 10 ]. The advanced structure and applications of FC are driven by a new technology that requires more benefits which can only be provided if the applications are used next to the end users, as in the following research intensive domains:

IoT : FC is particularly well-suited for IoT applications where large amounts of data are generated by sensors and devices. By processing data locally at the edge, FC can reduce latency and minimize the need to send all data to the cloud.

Smart Cities : FC can be applied to smart city initiatives by providing real-time processing and analysis of data from various sources such as traffic cameras, environmental sensors, and smart grids. This enables faster decision-making for city management.

Smart Healthcare : in healthcare, FC can be used for real-time monitoring of patients, processing data from wearable devices, and analyzing medical images locally. This helps in timely decision-making and reduces the load on centralized healthcare systems.

Autonomous Vehicles : FC is crucial for autonomous vehicles, where quick decision-making is essential for safety. Edge computing enables the processing of data from sensors on the vehicle itself, reducing the reliance on distant data centers.

Manufacturing: FC can enhance manufacturing processes by enabling real-time monitoring and control of machines on the factory floor. This can lead to improved efficiency, reduced downtime, and better overall production management.

Retail : in the retail sector, FC can be applied for inventory management, customer analytics, and personalized marketing. It allows for real-time analysis of customer behavior and preferences at the point of service.

Energy Sector : FC can be used to optimize energy distribution and consumption. Smart grids, for instance, can benefit from localized processing to manage power distribution efficiently.

Security and Surveillance : FC enhances security and surveillance systems by enabling real-time video analytics and threat detection at the edge. This is particularly important in critical infrastructure protection.

Agriculture : precision agriculture can leverage FC for real-time monitoring of soil conditions, crop health, and weather patterns. This enables farmers to make data-driven decisions for better crop yields.

Emergency Response Systems : FC can improve the efficiency of emergency response systems by processing data from various sources, such as sensors and social media, at the edge. This helps in quick decision-making during crises.

4.1 Smart marketing

Smart marketing is considered an important evolution of marketing, which is expected to drastically alter the way in which consumers engage with marketing as we know it. In marketing, the term ‘smart’ represents all things embedded in or enhanced by technology. Accordingly, whenever data are collected from different sensors, actuators, and machines within a marketing environment and access to and control of the data and the devices generating it are enabled through the internet, such a scenario may be termed a ‘marketing internet of things’ (MIoT). The MIoT in this sense will focus mainly on the transfer and control of mission critical information and responses and rely heavily on machine-to-machine communications. Recent developments in smart marketing include AI language models such as ChatGPT, Google’s Bard, and Meta’s LLaMA, all of which will provide new marketing data and new ways in which people interact with computers and each other [ 3 ].

Modern smart marketing research commonly employs “big-data” on huge amounts of observations across subjects, brand SKUs, predictor variables, and periods, producing large amount of different databases. For example, Amazon and Aliexpress have demands of data on millions of units selected and demographic definitions. Similar datasets were provided to retailing with potential deployment of RFIDs (radio frequency identifications), product reviews, social networking sites, mobile marketing, internet commerce, and customer requirements. Choice modeling of these huge datasets presents unique computational challenges, and sophisticated research tools for researchers. For example, using consumer’s feedback to the design of a product, so that the system can assist user-based service development [ 15 ]. The ability to classify marketing data and make significant judgments in the device’s own context will help in extracting important information from the massive amount of existing marketing data-bases. Therefore, the monitoring ecosystem can be modeled with the assistance of IoT and FC to minimize the latency in processing the large amount of marketing data. Thus, FC is an important research tool for many disciplines [ 16 ]. These applications showcase the versatility of FC in addressing the unique requirements of different industries and scenarios by bringing computational capabilities closer to the data sources. As FC technology continues to develop, we can expect to see even more innovative applications emerge in the years to come, including marketing research.

4.2 FC-supported marketing research

Data collection and processing : FC allows researchers to collect and process data at the edge of the network, closer to the data source [ 17 ]. Fog nodes will be able to perform real-time marketing data analysis.

Distributed computing and collaboration : FC will allow researchers to distribute computing tasks among fog nodes, enabling collaborative research efforts across multiple remote locations. This will promote collaboration, reduce the burden on centralized resources, and enhance marketing research productivity.

Real-time monitoring and control : FC can support real-time monitoring and control applications in research. For example, in environmental research, fog nodes equipped with sensors can continuously collect data on temperature, humidity, air quality, etc., and perform local analysis to detect anomalies or trigger immediate actions [ 18 ].

Edge analytics and machine learning : FC will enable researchers to deploy analytics and machine learning algorithms at the edge for real-time marketing decision-making and predictive analysis [ 19 ].

Mobile research applications : FC can support research applications on mobile devices, leveraging their computing power and connectivity. FC opens up opportunities for mobile-based data collection, surveys, and field studies frequently used in marketing research [ 20 ].

Resource optimization and energy efficiency : FC can help to optimize resource usage and energy efficiency in research projects. By distributing computing tasks and data storage across fog nodes, marketing research will be able to reduce the load on centralized servers and cloud infrastructure, resulting in more efficient resource utilization.

Privacy-preserving data analysis : FC will allow researchers to perform privacy-preserving data analysis at the edge. Instead of transmitting sensitive data to a central server, fog nodes can process data locally while preserving individual privacy. This is particularly relevant to marketing research, which frequently relies on personal or sensitive data, as well as voting and healthcare research, where privacy regulations need to be respected [ 21 ].

Hybrid capabilities : FC can deploy and combine data from different resources using an advanced monitoring infrastructure (AMI), in which smart grids deploy smart meters and sensors to collect various types of data. This will yield new measures of consumers’ mental state and behavior, which will facilitate screening, diagnosis, and ways of approaching consumers.

Concept testing : marketing research techniques used to screen new ideas do not have ample realism regarding concept presentation, competitive context, and measurement of consumer behavior to properly evaluate the importance of innovation. Without proved evidence of consumer demand for significant product change, firms can use FC. For example, hybrid approaches employ a combination of data derived from consumers’ digital emotions, physical product interactions, monitored senses, such as touch, taste, and smell, as well as virtual reality simulations [ 18 ]. All offer several advantages over conventional research techniques for testing innovative marketing ideas by allowing researchers to monitor customers response to new product designs, packaging, promotions, and merchandising in realistic, competitive settings while preserving a large degree of control and confidentiality.

Adaptive Algorithms : Marketing research process can be highly dynamic and subject to environmental changes. FC can host adaptive algorithms that adjust process parameters in real-time.

Decentralized collaboration : Marketing research often involves collaboration among different research groups or institutions. FC can support decentralized collaboration by enabling efficient and secure sharing of data and processing resources among many collaborators.

Questionnaire design : the emerging technologies of FC-enabled language models and content analyses will provide researchers with questionnaires tailored to the investigated issue, context, and target respondents, in multiple languages [ 5 ].

Compliance : FC can help researchers to comply with regulations in a number of ways, such as by ensuring data sovereignty, that is, these data are stored and processed in a way that respects the laws and regulations of specific country, while enhancing auditability by providing a clear trail of data-processing activities [ 4 ].

FC-supported nanomarketing : nanotechnology and FC may play an important role in marketing research, including the development of FC-enabled nanotechnologies for marketing research. Nanotechnology has the potential to revolutionize many marketing technologies, such as smart carts and neuromarketing [ 22 ]. Indeed, nanoscience is establishing a new marketing frontier – nanomarketing. This is highlighted in the development of new nanodevices and materials with improved properties. Nanoscale devices will be engineered to have a wide range of properties, including strength, flexibility, conductivity, and biocompatibility with unprecedented sensitivity and selectivity, all with the potential for use in a wide range of FC marketing applications, for example, collecting data from the human body, physical entities (e.g., products, ads, and shelves), and locations (e.g., stores) ( Figure 3 ) and transmit it to the FC for analysis. These unique data could be used to monitor emotions and behavior and deliver personalized products and messages.

Fog computing supporting marketing research.

In sum, FC promises to transform marketing research by deploying new types of monitoring systems, generating new types of data analysis, and informing how and where value is delivered. The emergence of, for example, FC-enabled MIoT/AI/nanotechnologies alone and in combination will increase the amount of data gained through connected (nano) devices and smart monitors, will enhance researchers experience, and widen the scope of marketing analytics. The inception of FC may entail a unique set of metrics for measuring marketing effectiveness beyond what has been employed in the existing marketing research domain.

5. Future research

Marketing research can employ FC in its various facets to generate better outputs in research and practice. It is acknowledged that this is a concept that has not been tested hitherto in marketing. Accordingly, we call here for future research and development to fully explore and realize FC’s marketing potential. Although algorithmic improvement is noteworthy in the case of marketing research along with novelty in applications, marketing research using the FC paradigm is a completely novel development, which exposes a prominent research gap . As research continues, market stakeholders will surely grapple with many innovative applications and research questions, and as nanoscience develops, it is likely to be combined with advanced computing methods such as FC, which will significantly impact marketing research in the years to come. With respect to this challenge, as AI decision support systems and other applications are currently under much criticism, it is important to investigate which market stakeholders are more inclined to adopt FC-enabled AI and which feel more vulnerable in the face of this emerging technology. Also, technological advances have resulted in a hyperconnected world, requiring a reassessment of FC-enabled marketing research from the perspectives of organizations, consumers, and society. However, large scale marketing analytics should be deployed to check the robustness of the proposed FC-enabled marketing research framework. In addition, there are many relevant research questions, linked to the framework, which might be posed in future marketing research as the following:

RQ 1 : How should marketing research and FC collaboration resonate with consumers?

RQ 2 : How can marketing research scale relative management and customers’ trust in the different data-generating devices?

RQ 3 : How can marketing research utilize the FC architecture for maximizing customer satisfaction, market share, and profitability?

RQ 4 : How can FC include emotional monitors, like emotionally intelligent machines, regarded as very important to marketing research? [ 21 ].

RQ 5 : What kind of research monitors and services can be integrated into the FC platform?

RQ 6 : Are all data privacy and security problems significant to marketing research solved by FC?

RQ 7 : How can FC fully integrate and coordinate among the various mobile fog nods?

RQ8: How can a blockchain and FC collaborative design support marketing research and strategy? [ 23 ].

EQ 9 : How can FC incorporate situational information for generating effective research recommendations?

RQ 10 : What FC-enabled virtualization techniques should be used for marketing research?

6. Conclusions

One of the most remarkable changes brought about by FC is the unprecedented interactive system that cuts across physical, sensory, virtual, digital data, not only for researchers but also consumers, products, stores, locations, and store shelves. Research on FC remains in its initial phases. Throughout the article, we have provided a holistic view of FC as a smart world facilitator. FC is a relatively new paradigm that has swiftly garnered wide recognition and found broad applications, due to its significant contribution to advanced computing technology (an updated list of references can be obtained from the authors). However, there is a clear gap in marketing of not gaining from the significant potentials of FC to research. In other words, the ubiquity of FC usage compels marketing research and scholars to study its effects on the vast array of marketing and consumer research domains. Key et al. ([ 24 ], p. 162) claimed that “Marketing scholars tend to ignore new technologies, and this is particularly true of new information technologies….” FC is a novel technology to computing that can assist marketing research to transcend the issues imposed by usual cloud computing. The many computing qualifications of FC are clear, such as IoT analysis, reduced transmission costs, and superior real-time reply, which might have a major impact on operation of supply chains, cybersecurity services, manufactures, firms, and even the quality of peoples’ lives. In addition, since its introduction, FC has a major impact on research on the quality of service (QoS). However, with the inception of the IoT and AI services and technologies, which introduced a new “smart world” where many things are automatically monitored, supplying only quality of service is unacceptable as it does contribute to a gratifying experience to users. FC will continue to develop and position itself as an important technology for autonomous systems, which will affect the performance of new and emerging smart applications, like smart products, immersive supply chain, and autonomous smart retailing [ 12 ]. Positioned as an important contribution within the emerging FC-focused literature, this article, we hope, will serve as a valuable reference for marketing scholars and practitioners and foster future theoretical and practical research innovations in FC-driven marketing. There are many opportunities for marketing research and strategy. Smart marketing can exist and grow only when they continuously and rapidly adapt to changing research and analytical technologies. We hope that the present article will encourage marketing scholars to adopt this nascent platform to advance marketing research and theories. In much the same way as marketing have moved into cloud computing, FC is the natural next step. Just as the fog can make an instantaneous decision to turn traffic lights green when it detects emergency lights flashing on an ambulance, or as smart vehicles use a large amount of data to enable customer’s safe driving, FC can assist smart marketing research by improving research methods and managerial decision making.

• 1. Economist. The Era of cloud’s Total Dominance Is Drawing to a Close. The Economist Group; 2018. Available from: https://www.ecomomist.com/business/2018/01/18
• 2. Herhausen D, Bernritter SF, Ngai EW, Kumar A, Delen D. Machine learning in marketing: Recent progress and future research directions. Journal of Business Research. 2024; 170 :114-131
• 3. Liu Y, Alzahrani IR, Jaleel RA, Al Sulaie S. An efficient smart data mining framework based cloud internet of things for developing artificial intelligence of marketing information analysis. Information Processing & Management. 2023; 60 (1):103-121
• 4. Costa B et al. Orchestration in fog computing: A comprehensive survey. ACM Computing Surveys (CSUR). 2022; 55 (2):1-34
• 5. Tran-Dang H, Kim DS. Cooperative and Distributed Intelligent Computation in Fog Computing: Concepts, Architectures, and Frameworks. Cham: Springer Nature Switzerland; 2024
• 6. Li S, Liu H, Li W, Sun W. Optimal cross-layer resource allocation in fog computing: A market-based framework. Journal of Network and Computer Applications. 2023; 209 :103-124
• 7. Liu X, Singh PV, Srinivasan K. A structured analysis of unstructured big data by leveraging cloud computing. Marketing Science. 2016; 35 (3):363-388
• 8. Taylor M et al. Internet of things support for marketing activities. Journal of Strategic Marketing. 2020; 28 (2):149-160
• 9. Sofla MS et al. Towards effective offloading mechanisms in fog computing. Multimedia Tools and Applications. 2020; 1 :1-46
• 10. Tomar R et al. Fog Computing Concepts, Frameworks, and Applications. USA: Chapman and Hall/CRC; 2022
• 11. Manzoor A et al. Multi-tier authentication schemes for fog computing: Architecture, security perspective, and challenges. International Journal of Communication Systems. 2022; 35 (12):e4033
• 12. Santos J, Wauters T, De Turck F. Efficient Management in fog Computing. In: Network Operations and Management Symposium. Miami, FL, USA: IEEE; 2023. pp. 1-6
• 13. Singh J, Singh P, Hedabou M, Kumar N. An efficient machine learning-based resource allocation scheme for SDN-enabled fog computing environment. IEEE Transactions. 2023; 44 (2):122-145
• 14. Ahmed U, Lin JCW, Srivastava G. A resource allocation deep active learning based on load balancer for network intrusion detection in SDN sensors. Computer Communications. 2022; 184 :56-63
• 15. Naik P et al. Challenges and opportunities in high-dimensional choice data analyses. Marketing Letters. 2008; 19 (3):201-213
• 16. Nezami M et al. Shareholder wealth implications of software firms’ transition to cloud computing: A marketing perspective. Journal of the Academy of Marketing Science. 2022; 50 :522-538
• 17. Aliyu F, Abdeen MA, Sheltami T, Alfraidi T, Ahmed MH. Fog computing-assisted path planning for smart shopping. Multimedia Tools and Applications. 2023; 82 (25):38827-38852
• 18. Chang V, Sidhu J, Singh S, Sandhu R. SLA-based multi-dimensional trust model for fog computing environments. Journal of Grid Computing. 2023; 21 (1):4-24
• 19. Gupta R, Singh A. Fog computing framework: Mitigating latency in supply chain management. In: Fog Computing. USA: Chapman and Hall/CRC; 2023. pp. 205-211
• 20. Assad A, Khan SU, Zomaya AY. Fog Computing: Theory and Practice. USA: John Wiley and Sons; 2020
• 21. Caruelle D et al. Affective computing in marketing: Practical implications and research opportunities afforded by emotionally intelligent machines. Marketing Letters. 2022; 33 :163-169
• 22. Mileti A, Guido G, Prete MI. Nanomarketing: A new frontier for neuromarketing. Psychology and Marketing. 2016; 33 (8):664-674
• 23. Jain D et al. How is blockchain used in marketing: A review and research agenda. International Journal of Information Management Data Insights. 2021; 1 (2):100-124
• 24. Key TM et al. Marketing’s theoretical and conceptual value proposition: Opportunities to address marketing’s influence. AMS Review. 2020; 10 (3):151-167

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