Promoting Organizational Justice In Cross-Cultural Data Collection, Analysis, And Interpretation: Towards An Emerging Conceptual Model

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MarginalizationInstructional DesignCross-culturalEmpirical Data CollectionHuman Performance ImprovementEthical MethodologyOrganizational Justice
Human performance improvement (HPI) practitioners, including Instructional designers (IDs), typically strive to inform inclusive, equitable, and socially just organizational development, workplace learning, and performance improvement decisions when working across cultures. The intention behind these types of decisions is to avoid causing harm to organizational members and the larger societies they serve. One way researchers, IDs, and HPI practitioners can support inclusive, equitable, and socially just organizational decision-making is by operating under organizational justice theory. In this work, we describe how organizational justice theory can be applied by practitioners in cross-cultural data collection, analysis, and interpretation project work.


Recently, human performance improvement (HPI) professionals, instructional design (ID) practitioners, and scholars have started to focus on cross-cultural data collection, including overcoming cross-cultural barriers and collaboration (Sands et al., 2007; Peters & Giacumo, 2020). Simply put, cross-cultural data collection takes the form of interviews, surveys, questionnaires, focus groups, and other approaches in which HPI professionals, IDs, and scholars collect data in cultural contexts from which they do not originate (Peters & Giacumo, 2020). Ethical and methodological challenges are apparent in cross-cultural data collection; there are often barriers that emerge due to tension between cultural identities, unequal power dynamics, gaps between ethical guidelines across academia, and the ethical norms in different cultures (Peters & Giacumo, 2020). These issues are particularly problematic if the researchers are from a different culture than their research participants (Shordike et al., 2017, p. 286) or the context of the instructional design (Romero-Hall et al., 2018).

One recommendation for improving cross-cultural data collection is to include multiple perspectives in decision-making. A number of studies show how including multiple perspectives from all relevant components of an organizational system and individuals from selected populations can help improve workplace learning, general operations, and organizational performance (Asino et al., 2017; Breman & Giacumo, 2020; Breman et al., 2019; Liamputtong, 2008; Peters & Giacumo, 2020; Peters & Giacumo, 2019; Ramos-Burkhart, 2013; Smeds & Alvesalo, 2003; Young, 2008). Yet, notably missing from the performance improvement standards and literature is any direct discussion regarding how organizational justice issues or power differentials can affect organizational settings, culture, and performance improvement goals, as well as the organizational positions and/or societal experiences of an organization’s members.

We posit that the gap in the literature exists because including multiple perspectives and representatives of stakeholder groups in the research process is not enough. As Guerra (2006) remarked, “The field of performance improvement should . . . help practitioners add a demonstrable value to the field and society as a whole.” (p. 1025). To add demonstrable value to the field and society, attention must also be spent on ensuring that cross-cultural data collection is grounded in principles of fairness, inclusion, and dignity.

Asino & Giacumo (2019), noted how it is important for practitioners to enable performance improvement and workplace learning that is responsive to the unique cultural needs of specific target populations. Errors made in cross-cultural contexts can cause harm to individuals and their relationships with others, as well as ineffective interventions and financial strain on organizations (Littrell & Salas, 2005). However, Peters & Giacumo (2020) noted a dearth of guidance for researchers and practitioners collecting data across cultures. This lack of guidance can be detrimental to organizational performance and workplace learning efforts in multinational and national organizations.

The purpose of this paper is to offer HPI professionals, which includes instructional design (ID) practitioners and scholars, guidance on achieving organizational justice in cross-cultural data collection by being responsive to the cultural needs of individuals and communities. We started with the guidelines offered by Peters & Giacumo (2020) but realized that it was not designed for issues of social justice. Hence, we combed the literature on organizational justice and used that lens to integrate social justice within Peters & Giacumo’s (2020) guidelines. With this new organizational justice lens, we propose an augmented conceptual model in this article. To achieve this aim, we consulted existing professional standards that offer insight into promoting organizational justice. Therefore, our new combined conceptual model offers HPT practitioners guidance to support cross-cultural data collection and analysis that is equitable, inclusive, and socially just.

This study is significant because although some theoretical methodological guidance for conducting HPI research and practice across cultures is emerging, more research is needed to advance equity and inclusion practices in HPI. Organizational leaders, researchers, and practitioners can each have more power than the individuals they support through ID and performance improvement interventions. This power differential may come from economic, historical, political, or other factors (Liamputtong, 2008). By understanding this differential, researchers and practitioners can begin to acknowledge and correct systemic oppression of marginalized populations and individuals both internal and external to the organizations they serve (Guerra, 2006; Morris and Bunjun, 2007). Therefore, we seek to address one research question: How can IDs and Performance Improvement Specialists support socially just decisions regarding workplace learning design and organizational performance improvement?

Cross-Cultural Data Collection Guidelines: Peters & Giacumo (2020)

There are a number of common barriers and issues that can arise before, during, and even after cross-cultural data collection takes place. Participants can be wary of the researcher because of the researcher’s demographics (age, sex, status, gender, class, race, ethnicity, etc.; Shah, 2004; Rubin and Rubin, 1995). Additional barriers include the interviewee not feeling comfortable discussing certain topics, concerns about confidentiality and power dynamics between themselves and the researcher, and ethical differences. (Adler et al., 2001 & Ryen, 2001, quoted in Sands, 2007, p. 355; Honan et al., 2013). Reluctance may also be based on concerns that members of the dominant culture will use the interview to further institutional agendas and legitimize social inequalities (Briggs, 2001, quoted in Sands, 2007, p. 355).

To help researchers and practitioners develop more inclusive and equitable practices, Peters & Giacumo (2020) presented a series of seven practical guidelines for practitioners who work across cultures as shown in figure 1.

Figure 1

7 Cross-Cultural Data Collection Guidelines

Four ethical guidelines and three methodological guidelines are shown to overlap.

These guidelines were drawn primarily from the fields of sociology and anthropology. They fell into two major, but overlapping, categories: ethical considerations (i.e., what should be) and methodological considerations (i.e., what should be done). Peters and Giacumo (2020) suggested four ethical considerations practitioners should consider when preparing to collect data across cultures, including how they can: build trust through a shared rapport (see also Jennings, 2005), add time to account for increased project complexity, demonstrate respect for cultural beliefs by intentionally considering their own cultural values through the practice of reflexivity (see also Levitt, 2015; Guerra, 2006), and take a participatory approach by treating the data collection as a collaborative partnership (see also Liamputtong, 2010). In addition to these ethical considerations, the authors suggest that there are three practical, methodological elements to successful cross-cultural interviews. These included how they can: ensure communication through effective use of language, translation, and nonverbal cues, employ fair sampling strategies, and ensure that informed consent takes into consideration the power differential that may exist between data collector and participant.

Missing from these guidelines, however, was a cohesive theory that could be utilized to support socially just decision-making and interactions in organizations and in cross-cultural data collection projects. Also missing was evidence from the literature showing how HPI practitioners might follow these practical guidelines and respond to the results they see in their field. In short, we saw that applying these guidelines alone might not reliably yield: 1) desired organizational performance improvement outcomes or 2) improve an organizational system to be more inclusive, fair, and just, both of which are central to our personal motivations for engaging in performance improvement.

Organizational Justice in HPI Data Collection Across Cultures: Synthesizing Existing Perspectives

In this section, we provide a brief review of literature. We begin with an overview of organizational justice theory and then summarize the standards and principles for organizational justice offered by various professional societies. The review underpins our emerging conceptual model and recommendations for work towards achieving organizational justice with cross-cultural data collection.

Organizational Justice Theory

Social justice is most commonly understood as the relative balance or fairness between individuals as well as large social groups, through comparing wealth, liberties, and equal opportunities (Banai et al., 2011). Greenberg (1990) suggested that early theories of social justice applied to organizations have evolved into the concept of organizational justice. Organizational justice theory suggests the more organizational members perceive decisions and interactions within the organizational environment as fair and just, the more engaged and productive they and the organization become. Members’ perception of fairness, which is part of the practice of organizational justice, influences organizational citizenship and decreases counterproductive behaviors (Latham & Pinder, 2005), and increases job satisfaction and organizational loyalty while decreasing turnover (Fatt et al., 2010).

Recent researchers point to evidence that organizational justice affects a variety of aspects of organizational performance (Ambrose & Cropanzano, 2003; Moon, 2017) and workplace learning (Oh, 2019; Sartti, 2019). Researchers have investigated this theory across a wide variety of cultures and organization types (Zaman et al., 2010; Al-Zu'bi, 2010). Researchers also use this term to describe a broad category of study including fairness, equity, ethics, equality, and behaviors in organizations (Colquitt, 2008; Cropanzano & Stein, 2009; Hoy & Tarter, 2004). Organizational justice is also known as the relative balance or fairness and moral or ethical treatment of individuals within an organization (Cropanzano et al., 2007; Rausch et al., 2005).

One commonly accepted model of organizational justice consists of three dimensions: distributive, procedural, and interactional justice (Karriker & Williams, 2009). Distributive justice is the relative balance or perceived fairness of outcomes such as compensation, office assignment, promotions, job titles, and other similar decisions that affect members of an organization (Karriker, & Williams, 2009). Procedural justice is the relative balance or perceived fairness of the processes through which outcome distributions happen vs. “how the systems or procedures ‘should’ operate” (Karriker & Williams, 2009, p. 114). Interactional justice is the relative treatment of interpersonal communication (Simmons, 2010), which is “usually operationalized as one-to-one transactions between individuals” (Cropanzano et al., 2002, p. 329) often with regards to courtesy, respect, honesty, and dignity (Weldali & Lubis, 2016). We can draw upon these theoretical constructs of justice to frame HPI practices.

Professional Standards and Principles Associated with Organizational Justice

Guerra (2006) remarked that HPI professionals add value by consulting with clients in ethical considerations as well as performance standards. Although there are a number of sources that can guide organizational justice in data collection, we draw largely on The International Society for Performance Improvement’s (ISPI) and the Academy of Human Resource Development (AHRD) in this paper because are considered the go-to sources for practice in the field of instructional design and performance improvement.

Four of ISPI’s Ten Standards (n.d.) address organizational justice directly. These include: (1) take a systemic view, (2) ensure solutions’ conformity and feasibility, (3) add value, and (4) work in partnership with clients and stakeholders (ISPI, n.d.), and (5) the integrity principle. In other words, practitioners should be inclusive, aware of social and cultural factors, and ensure the recipient recognizes and benefits from the intervention.

The first two standards, take a systemic view and ensure solutions’ conformity and feasibility, specifically direct practitioners to examine the relationship between context and the proposed intervention. In practice, this means that if IDs or HPTs need to collect data, they will possess at least a base level of understanding of local laws, customs, and politics so they can ask interviewees thorough questions. The third ISPI (n.d.) standard, add value recognizes “competent practitioners” as those who can improve project outcomes through their expertise. When working with clients who wish to improve workplace learning or organizational performance in a cross-cultural setting or avoid causing unintentional harm to members of marginalized groups, they are able to better estimate the time and effort required for data collection processes. Therefore, they are better able to estimate risks and costs associated with their data collection plan. The fourth standard, work in partnership with clients and stakeholders, requires practitioners to keep all parties involved in each part of the project.

ISPI’s (n.d.) integrity principle requires honesty and truthfulness in our work with clients and others (Guerra, 2006). In collecting data from participants who are from another culture or marginalized groups, the practitioner will take additional time and resources to ensure the participants are informed about the risks and benefits of their participation and any proposed intervention. A trusting, collaborative, and informed interview will generate more accurate data and lasting partnerships (ISPI, 2021). In other words, practitioners should be collaborative, honest, and build trust between themselves, participants, and clients.

In addition, the Academy of Human Resource Development (AHRD) Standards on Ethics and Integrity list 5 general principles for professionals. Namely, professionals recognize the boundaries of their own competence, respect people’s rights and dignity including their privacy and confidentiality, are aware of racial, socioeconomic, language differences and refrain from discriminatory practices, and take on a social responsibility to promote human welfare (AHRD, n.d.).

A Conceptual Model for Organizational Justice and Cross-Cultural Data Collection: Blending Organizational Justice Perspectives with Cross-Cultural Data Collection Practices

In light of this gap in research and practice to advance equity and inclusion in ID and HPI work, we are taking Peters and Giacumo’s (2020) practice guidance for cross-cultural interviewing and framing it within organizational justice to build a conceptual model for data collection. We see this as a new conceptual model that brings new considerations to light. Those considerations emerge when the constructs of organizational justice – that is, distributive, interactional, and procedural justice - become central to the Peters & Giacumo (2020) initial practice guidance. For example, when HPI practitioners and scholars work towards achieving distributive justice, or balance equity, equality, and needs. During decision making, organizational members would become more satisfied with decisions and their outcomes (Yang et al., 2019). When HPI practitioners and scholars work towards achieving procedural justice, or improve policies, procedures, and processes, for all groups of affected stakeholders, organizational members become more satisfied with the organizational system and subsystems (Kim & Beehr, 2020; McCluskey et al., 2019). HPI practitioners and scholars can also work towards achieving interactional justice, which entails improving how individuals are treated in interpersonal and informational communications to ensure they are treated with respect, kindness, politeness, dignity, and transparency, and access to information (Siachou et al., 2021). This has been associated with organizational members becoming more satisfied with their relationships in the organizational system and subsystems, thus leading to improved performance (Ahmad, 2018; Leineweber et al., 2020).

Further, as shown in figure 2, each of these three justice types overlap with each other. This means that perceptions of one type of organizational justice can mediate or influence another type of perceived organizational justice (Zhang et al., 2017). For example, Rhoades et al. (2001) noted that interactional justice in the form of supervisor support has been found to affect perceived distributive justice as their treatment can be ascribed to the organization’s policies. Johnson et al. (2014) noted how engaging in procedural justice work can be costly and draining, resulting in less ability to regulate emotions and thus decreased perceptions of interactional justice. Posey et al. (2011) found that perceptions of procedural injustice arising from computer monitoring activities influenced perceptions of distributive justice. Thus, the three types of justice (i.e., procedural, distributive, and interactional) together influence perceptions of organizational justice.

Figure 2

Three Components of Organizational Justice

Procedural justice, distributive justice, and interactional justice are overlapping components of organizational justice theory.

Adding these constructs to the Peters and Giacumo (2020) guidelines brings a new lens of social justice into focus. While an HPI project may be untaken with the goal to improve an organization’s performance through addressing related policies, procedures, processes, and informational communications, which have been at the heart of HPI work since its inception, it should be not only ethically responsible but also socially just. In effect, we posit that HPI practitioners have an added responsibility when working with organizations to work towards organizational justice, as this is also correlated with improved organizational performance. To justly advance organizational performance, HPI practitioners would also explicitly address issues related to equity, equality, human needs, and interpersonal communication, in their work with stakeholders in an organization. In short, the HPI projects would be done in ways that yield fair, equitable, and culturally responsive outcomes, through respectful, transparent, communications, and do not overburden any stakeholders or individuals they are meant to serve.

With this new lens, each of the original seven guidelines would take on a new light. For example, Peters and Giacumo (2020) recommended to build trust with our clients and stakeholders, suggesting to recognize the sensitive nature of historical power differences between groups (see also Chistopher et al., 2011), taking time to answer questions authentically, while communicating the associated benefits and risks with those affected by our work (see also Guerra, 2003). If we center trust building in distributive justice, we may also only agree to work for clients in organizations [or on project scopes] who [that] would endeavor to work towards equitable or needs-based resource allocation aligned with organizational performance improvement goals. And, also to share these goals in our communications with all stakeholders for purposes of accountability.

Even taking a participatory approach would take on a new responsibility when distributive and procedural justice are considered. Traditionally, you would have participants play a role in determining what is important in a project and/or shaping data collection, analysis, and reporting methods. There’s typically still a lack of awareness or consideration of the benefits and drawbacks of engaging in this kind of work. if you don’t also include the client organization and/or participants in discussions about the longer-term change implications for equity, equality, needs, policies, procedures, and processes in the organization and community. For example, a tradeoff of building more efficient transportation systems or infrastructure can affect businesses, communities, and families, when for example, historic routes or time frames are altered and have potential to become isolating for at least some. Tradeoffs should be made transparent and clear to participants early on and chosen with appropriate buy-in.

With the addition of these new organizational justice constructs, we augment the Peters and Giacumo (2020) guidelines into a conceptual model. Along with this augmentation, we also suggest three updates to the Peters and Giacumo (2020) guidelines. The first change that will be revealed in the conceptual model is to revise one component name from the former model – informed consent. The second change is to add one new component to the conceptual model – plan for logistics. The third change is to share empirical evidence to further substantiate the component named add time. These updates, described below, reflect the need for a conceptual model language that applies to practitioners and not just academics.

Informed Consent

Previous research situated in academic contexts used the term informed consent (Peters & Giacumo, 2020; Liampatoung, 2008). This specific term is tied to the guidelines under institutional review boards to mitigate risk, ensure safety, protect participants' confidentiality, and respect participants’ privacy, which often requires soliciting informed consent (“Institutional Review Boards Frequently Asked Questions,” 1998). However, as Bies (1993) notes, organizational justice theory, specifically procedural justice, speaks more broadly to care for protecting the safety, confidentiality, and privacy of organizational members. This extends beyond the IRB framework, which only refers to direct study participants. Further, with interactional justice and the dignity of our clients in mind, we can also change the title so that practitioners are better able to recognize the application in practice without a sole reliance on jargon that’s familiar only to academics. Therefore, one update we make in the conceptual model we introduce in this article would be to add “informed participation consent to ensure safety, confidentiality, protect privacy, and describe potential benefits and limitations” and specify that all stakeholders, all organizational members, and participants, should be made aware of these associated methods and project plans.

Plan for Logistics

We suggest adding plan for logistics as one new component of the conceptual model we introduce in this article as well. We make this recommendation because the planning required to collect data across cultures often requires acquiring new expertise, even for those who are familiar with data collection planning in their own cultures. Researchers have shown that infrastructure such as roads, internet access, phone systems, postal services, electricity access, even the ability to gather in any single physical location, can be limited or change frequently and often unexpectedly (Breman et al., 2019, Gitau et al., 2010; Mercer, 2004; Rao, 2005). Our own research and HPI project work also confirms a need for more informed logistical planning.

Add Time

Last, we expand upon the component add time in the augmented conceptual model we share in this article. While Peters & Giacumo (2020) illustrated why additional time would be necessary in a cross-cultural data collection effort, they did not point to any previous research in their article that had explicitly stated that additional time is necessary. However, that may have been a limitation of their literature review process because a closer look does reveal that literature does point to the significant amount of time required to conduct valid performance improvement projects across cultures (Bamberger et al., 2010; Cullen et al., 2011; Powell et al., 2010). It should be noted that these two components, plan for logistics and add time have a strong relationship. We have again found that in order to overcome logistical issues, the researcher or practitioner may have to add more time into the data collection process.

Combined Organizational Justice and Cross-Cultural Data Collection Conceptual Model

As shown in Figure 3, we propose a visual representation of the emerging combined organizational justice and cross-cultural data collection conceptual model that further demonstrates the relationships of the perspectives combined in this paper. At the center of Figure 3 is the theoretical model of organizational justice, including procedural, distributive, and interactional justice (DeConinck, 2010). Achieving organizational justice in cross-cultural data collection helps to ensure the organization’s decisions are trustworthy (DeConinck, 2010). For example, one can undertake a participatory approach and not plan for appropriate sampling or safety and then not meet the goal to contribute to a trustworthy, and socially just organization.

Figure 3

Organizational Justice and Cross-Cultural Data Collection Conceptual Model

This figure illustrates the components of the organizational justice and cross-cultural data collection conceptual model.

We center this model within the components of cross-cultural data collection because the approaches we describe to gather valid and reliable data rest squarely on the principles of fairness, equity, equality, and ethics. That is to say that for a socially just organization to achieve distributive justice, the resources required for data collection efforts, both economic and social, must be allocated fairly (Ferrell & Ferrell, 2008). Similarly, for a socially just organization to achieve procedural justice, the data collection systems and procedures used to obtain outcomes, would be configured so as not to overburden or under-benefit any single person or group (Ferrell & Ferrell, 2008). Interactional justice, or the way individuals are treated, has been positively related to knowledge-sharing behaviors in organizations (Li et al., 2017). Li et al. (2017) suggested that if data collection efforts are welcoming and inclusive, or participatory in nature, while maintaining all stakeholders’ and participants’ dignity, respect, and safety, more knowledge would likely be shared.


Below are six practical implications and recommendations for researchers and practitioners and one practical implication for organizational leaders. Each implication is linked to at least one component of organizational justice (e.g., distributive justice, procedural justice, interactional justice). These implications can be used by practitioners, scholars and leaders alike, to help ensure project designs communicate these considerations with all stakeholders.

Involve Local Representatives and Translators When Translators Are Included

We recommend identifying representatives who are familiar with both cultures, and translators from the local culture, who are interested in working on the project before data collection and analysis. This recommendation aligns with interactional justice, by showing respect for the information and time that participants shared. Ideally, we recommend including these individuals in project planning and scoping who are also familiar with the desired performance domain.

Translators from the community are better adept at understanding the dialect, nuance, politics, and beliefs of the culture. A local translator may also help with access to people that might otherwise be wary of outsiders. In the data analysis phase, translators can also be helpful in explaining cultural norms, tone, and meaning. Involving locals in your project is generally recommended to attain buy-in from your participants. Similarly, even when a translator is not included in a project a local representative who knows both cultures will be able to interpret meaning and nuances you may otherwise miss (Breman et al., 2019).

Discuss Long-Term Project Implications for Participants And All Stakeholders

By taking a participatory approach early on, practitioners and academics can have more confidence that their project will be accepted by their participants and other stakeholders. However, applying distributive justice to a participatory approach from the project inception will likely lead to more useful, sustainable, and appreciated project outcomes. This means that stakeholder and participants’ direct input and potential experiences are considered when prioritizing needs, desired outcomes, and potential future pathways forward. It also means that serving the principles of equity, equality, and human needs, elevates the importance of planning for a robust sampling strategy. This is a great way to build trust in the community or organization through ensuring you build in processes and procedures for collecting this input as part of your approach towards procedural justice.

Explain Protections, Potential Benefits, and Limitations to Participants

Plans should start early in the project design to protect participants’ confidentiality, privacy, safety, and ability to share. This is an applied example of a procedural justice in action (e.g., policies, procedures), ensuring a supportive environment free from retribution and supportive of optimal learning, development, and organizational performance. To demonstrate respect and dignity (e.g., interactional justice), participants should be able to understand these plans, which may require translation into their language. This work will likely also help participants to perceive distributive justice when decisions are made later on regarding resource allocation, which can support their satisfaction with the outcome or decisions as a result of their participation.

Before soliciting information from participants, professionals should inform the participant of the purpose of the data collection, the benefits and risks of participating, plans to maintain participants’ confidentiality and any associated limitations with these plans; how personally identifiable information will be secured, how the results will be shared, and how the raw data will be disposed (Peters & Giacumo, 2020).

Offer to Conduct Data Collection in A Location Or Mode That Is Most Comfortable For The Participants

Participants may have different histories and cultural norms for sharing information. Practitioners also have to consider the power dynamics between themselves, data collectors, and participants. We recommend asking the participant to choose the data collection location at a place (e.g., physical location, time) or mode (e.g., in person, by phone, by video conference) most comfortable or convenient for them. This recommendation is aligned with interactional justice (e.g., can increase relationship satisfaction) and procedural justice (e.g., can increase system satisfaction).

Build Extra Time into Your Project Schedule

Practitioners and researchers should recognize the extra workload associated with collecting data across cultures. Overcoming language barriers and logistical issues such as finding a local translator or traversing muddy roadways will obviously require more time. What may not be so obvious is the additional time needed to ensure ethical and responsible data collection. When working with marginalized groups, especially those that are wary of outsiders, building trust, gaining access, and forming partnerships will take more time.

It can be challenging to estimate how much “more time” one will need, which can hamper efforts to secure additional funding or resources from organizations and donors. However, the practitioner and researcher have a responsibility to recognize their own limitations. If more time cannot be allotted to overcome methodological and ethical issues, data collected may be overly biased, unreliable, and invalid. More importantly, rushing through ethical and methodological considerations can harm the organization and individuals you are working with and undermine or prevent future opportunities.

Practitioners and researchers can employ a few tactics to address time concerns by working with local representatives early in the scoping process to determine risks and appropriate mitigations, the feasibility of the project plan, if there is interest in the potential outcomes, and if people are willing to participate.

Making space for this additional time will likely lead to higher perceptions of organizational justice all around. More valid and reliable data can support both distributive justice (e.g., decisions regarding resource allocation, equity, equality, and human needs) and procedural justice (e.g., decisions regarding policies, procedures, and processes). Sharing information and updates regarding the project timeline and the work being done to consider the client’s and stakeholders’ needs can also support interactional justice.

Start Building Trust Early

Practitioners working across cultures should determine how to build trust with the groups and individuals they will work with, recognizing that different approaches may be better suited for one group of people, but not appropriate for another group. For western researchers and practitioners working in marginalized communities, building trust can be more challenging. Ethnic and racial minorities, for example, may be hesitant to speak with project personnel because of past injustices. In these situations, you should plan to spend additional time building trust aligned to the ways trust is created in any specific group or community(ies), which demonstrates diligence towards achieving distributive justice.

Thus, we must be aware of how we are viewed by those whom we endeavor to serve and how those views can affect data validity. Not only will this allow for better data collection planning approaches in any organization but also considers the sensitive relationship between organizations headquartered in affluent societies and organizations from historically marginalized societies or backgrounds. With this information, we will likely work towards improved interactional justice as our communications can be more cohesively designed to better demonstrate respect, dignity for the individuals, organizations, and societies we serve. Therefore, we can draw upon this conceptual model not only to facilitate an understanding of power, but to push towards respectful and feasible solutions in our theoretical and organizational research (Morris and Bunjun, 2007).

Set Organizational Standards to Achieve Distributive and Procedural Organizational Justice

Thus far, our implications have been directed at practitioners and academics gathering data in cultural contexts they may be initially unfamiliar with. These changes cannot be effectively implemented, however, without the support of the sponsoring organization or community. Sponsored projects will struggle to meet their methodological and ethical considerations as long as businesses and universities fail to accept organizational justice as a guiding principle. Therefore, sponsoring organizations should develop a supportive organizational culture, ethical standards, and methodologies, for employees working across cultures. This would facilitate individuals’ work that prevents harm to participants, improves data validity, and achieves better outcomes, including return on investments or return on expectations. In addition, adoption of an organizational justice component to a data collection model can support employees by allowing more clear expectations and resource allocation for projects. The model should be considered a “jumping off point” for discussion. Further discussion of best practices for protecting participant privacy, while also delivering results to clients, could be a useful way for practitioners to share knowledge and develop professional skill sets.

For Future Research

This is a preliminary study to initiate development of a conceptual model designed to facilitate achieving organizational justice in cross-cultural data collection. The findings support the initial literature review conducted by (Peters & Giacumo, 2020). Additional research should consider attempts to validate or extend the practical guidance that application of this model can offer, further development to potentially lead to a more robust conceptual model of the approaches described, and stronger links between perceptions of organizational justice and cross-cultural data collection project organizational performance improvement outcomes. Further investigation could also demonstrate the extent to which researchers and practitioners believe in the need to implement components of the model in their data collection planning. This type of validation could then facilitate building a competency model to guide researchers and practitioners. Additionally, future work could be positioned to ask practitioners how they first learned to work across (workplace training, reading, personal interactions), which could provide more context for analysis.


As part of our reflective process before, during, and after the data collection process, we acknowledge how our self-identities as university-educated, white, female, may influence both our line of questioning and our conversations with research participants. We acknowledge that while it is impossible for any data collection method to be free of power dynamics, the researcher or practitioner can use data collection tactics that reduce harm to their participants and their organizations. We acknowledge that we have only been informed by previous research published in English and available in our university libraries. Further, there may be relevant research available that did not appear in our search results. Other research, especially in other languages, and library collections we cannot access, all may add new perspectives to our understanding in this area.


By tying ethical and responsible cross-cultural data collection methods into the theoretical constructs of organizational justice, we can begin to drive towards developing a more robust theoretically grounded model. The conceptual model provides a necessary foundation upon which to anchor effective workplace learning, instructional design, and performance improvement efforts. In order to engage in HPI work to benefit marginalized populations, one must engage in critical reflection on how to avoid unintended consequences and systemic oppression. In addition, there is potential to further explore a variety of conceptual models, as well as a set of validated best practices to guide project planning and implementation. More research is needed to explore the relationships between organizational justice theory and practical instructional design and human performance improvement applications.


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