Expectations of technology use during meetings: An experimental test of manager policy,device use,and task-acknowledgment

Abstract

In organizational meetings, mobile media are commonly used to hold multiple simultaneous conversations (i.e., multicommunication). This experiment uses video vignettes to test how manager policy (no policy, pro-technology, anti-technology), device use (notepad, laptop, cell phone) and task-acknowledgment (no task-acknowledgment, task-acknowledgment) affect perceptions of meeting multicommunication behavior. US workers (N = 243) who worked at least 30 hours per week and attended at least one weekly meeting rated relevant outcomes: expectancy violation, communicator evaluation, perceived competence, and meeting effectiveness. Results reveal manager policy and device use both affect multicommunication perceptions, with mobile phones generating the highest expectancy violation and lowest evaluation of the communicator and meeting effectiveness. Surprisingly, there was no effect for task-acknowledgment; however, a match between manager policy and task-acknowledgment affected evaluations. This paper unifies past evidence about multicommunication under the expectancy violations framework, extends theoretical understandings of mobile media use at work, and suggests practical implications for technology use in unfamiliar workplace situations.

Keywords

expectancy violations theory experiment materiality meetings multicommunication

Introduction

The average company in the United States faces an attention challenge whereby employees’ attention is pulled between face-to-face conversations and digitally mediated conversations (e.g., participating in a meeting while texting a friend). Employees use Information Communication Technologies (ICTs) as both a backchannel to communicate with those who are co-located and to communicate with a wide variety of communication partner(s) elsewhere. Engaging in multiple simultaneous conversations at once is a common practice called multicommunication (Reinsch et al., 2008; Turner & Reinsch, 2007). Formally, multicommunication is the process of “engaging in multiple conversations” simultaneously, often through ICTs (Turner & Reinsch, 2007, p. 37). Experts agree that the practice of multicommunication still significantly affects the workplace more than a decade after the concept was introduced (Anderson & Raine, 2018; Stephens, 2018).

Eighty-seven percent of companies expect employees to access a mobile application daily and 77 percent expect this need to increase (Syntonic, 2016). ICTs also expand the number of people employees communicate with outside of the organization, and two-thirds of US workers say phones positively affect their work (Smith & Anderson, 2017). Despite the ubiquity of mobile media, several studies suggest multicommunication is considered impolite, uncivil, or a signal of incompetence (Cameron & Webster, 2011; Jarvenpaa & Lang, 2005). However, multicommunicating through ICTs also has the potential to aid conversation (Reinsch et al., 2008) and can signal competent communication (Turner & Reinsch, 2007). It is not surprising that retrospective accounts position multicommunication as both beneficial and detrimental to workplace conversations (Turner & Reinsch, 2007, 2010). While on-task communication (e.g., “I’m emailing the vendor”) may be viewed positively, off-task communication (e.g., “My sister won’t stop texting me”) might be viewed as a distraction (Cameron et al., 2018). Still, perceptions of those engaging in multicommunication to compliment face-to-face communication remains understudied. Given the ubiquity of ICT use for work, the current study explores perceptions of multicommunicators in a meeting environment.

To test the competing outcomes of multicommunication, we leverage expectancy violations theory (EVT; Burgoon & Hale, 1988; Burgoon & Walther, 1990) and generate hypotheses about how different positive and negative expectancy violations might be interpreted by a meeting newcomer. Specifically, this experiment tests how manager policy preferences (no policy, pro-technology, anti-technology), device use (cell phone, laptop computer, notepad), and task-acknowledgment (acknowledge multicommunication as task-related, no task-acknowledgment) prompt evaluations of the multicommunicator and perceived meeting effectiveness. Below we present competing multicommunication findings, then introduce EVT to generate hypotheses for the experiment. Next, we discuss our method and results before we present the theoretical and practical implications of these findings.

Multicommunication

Research on multicommunication has primarily focused on retrospective or hypothetical accounts of multicommunication behavior. Multicommunication varies in terms of task-relatedness (Dennis et al., 2010), attentional focus (Cameron et al., 2018), and normative preferences (Stephens, 2018). Unlike phubbing, the general use of a mobile phone during interpersonal conversations (Kadylak, 2020; Roberts & David, 2016), multicommunication specifies that the communicator is engaging in multiple conversations at once, usually via an ICT (for a review, see Reinsch & Turner, 2019).

Multicommunication is a complex construct because it can be perceived as both helpful and harmful to a conversation depending on the situation. When multicommunication is perceived as helpful it is hypothesized to lead to a positive evaluation; in contrast, it may be negatively evaluated if ICT use distracts from the interaction (Turner & Reinsch, 2010). This is consistent with mobile media research suggesting mobiles both afford contact and create demands on users’ time (Mannell, 2019; Mascheroni & Vincent, 2016). If conversation is disrupted by multicommunication, the behavior is perceived negatively; however, if a communication partner extends the conversational scope or generates new information, this behavior can be perceived as useful. Therefore, multicommunication is dynamically capable of enhancing or detracting from communication in meetings.

Negative outcomes of multicommunication

Executives and workers both see mobile device usage as potentially problematic to work processes (Reinsch & Turner, 2019; Syntonic, 2016). Respondents recalling multicommunication episodes perceived multicommunicators as inattentive (Stephens & Davis, 2009), uncivil, less trustworthy (Cameron & Webster, 2011), and disengaged (Dennis et al., 2010). Retrospective survey data suggests off-topic multicommunication leads to less-effective meetings (Cameron et al., 2018). Additionally, engaging in multicommunication creates more errors in the focal and secondary conversations (Cameron & Webster, 2011; Cameron et al., 2018). Stephens (2017) summarizes, multicommunication “increases cognitive demands, impacts work quality, and affects working relationships” (p. 1654).

Positive outcomes of multicommunication

However, when multicommunication is task-related, the multicommunicator has been evaluated more positively (Cameron et al., 2018; De Bruin & Barber, 2019; Dennis et al., 2010). There is a positive association between multicommunication and topic-relatedness, as perceptions of meeting effectiveness are driven by the ability of the group to focus on the topic (Cameron et al., 2018). Multicommunication can increase feelings of team effectiveness (Dennis et al., 2010) and perceived availability (Turner & Reinsch, 2010). In sum, multicommunication can increase one’s availability, enable access to a wider variety of resources, and improve decision quality (Stephens, 2017).

Meeting participants face a tension between demonstrating attention to their colleagues and engaging in multiple conversations in both work and personal domains (Mascheroni & Vincent, 2016). Given the prevalence of multicommunication and competing outcome evaluations, expectancy violations theory (EVT) offers a framework to integrate disparate multicommunication findings.

Expectancy Violation Theory (EVT)

EVT serves as a useful theoretical framework to understand multicommunication behavior. Expectations are “cognitions about the anticipated communicative behavior” of others (Burgoon & Walther, 1990, p. 236). Expectations are normative and frame behavior for those interacting in a given situation, serving as the basis for evaluating communicator effectiveness.

Violations represent deviation from expected behaviors and can be positively or negatively valenced depending on the interpretation of the receiver (Burgoon & Walther, 1990). Valence is assigned to communication based on interpretation, relative to norms, and desirability of behavior. Valence is also impacted by how rewarding the behavior is considered to be. Positively and negative valenced violations are seen as rewarding and nonrewarding, respectively.

Burgoon (1993) theorizes that expectancy violations can be attributed to three normative judgments: context, relationship, and communicator reward. Context-based expectancies include the content-focus of interaction, task-orientation, and any other situational factors that “prescribe or proscribe certain interaction behaviors” (p. 32). Relationship expectations are based on power dynamics, similarity, or attraction between partners. Communicator sources of expectations are based on interpersonal evaluations of the partner including “demographics, personality, physical appearance [and] communicator style” (p. 32). The present test of multicommunication focuses on expectancy violations related to communicator- and context-driven expectations (i.e., manager policy expectations, device use, and communicator behaviors).

Existing research suggests that communicator’s traits, situational norms, and expectations all affect evaluations of multicommunicator behavior (De Bruin & Barber, 2019). Individuals who multicommunicate with technology appear competent or incompetent based on their allocation of technology in a meeting (Jarevenpaa & Lang, 2005). Further, multicommunication behavior affects perceptions of both the multicommunicator and the meeting effectiveness (Paskewitz & Beck, 2019). EVT provides an ideal framework for exploring competing behavioral perceptions; following EVT, we expect the valence of an expectancy violation to generate evaluations of both the communicator and the meeting more broadly. Competent meeting behavior evaluation is contingent on communicators appropriately allocating attention during the interaction (Turner & Reinsch, 2007). Together EVT and multicommunication research suggests relevant dependent variables to test meeting-related multicommunication: expectancy violation, communicator evaluation, perceived competence, and meeting effectiveness.

Making norms

Meeting multicommunication is the common organizational practice where “one is simultaneously engaged both in an organizational meeting and in one or more technology-mediated secondary conversation(s)” (Cameron et al., 2018, p. 306). Multicommunication is socially constructed, and thus affected by perceived norms and expectations set by managers and peers (Stephens & Davis, 2009). Stephens (2018) contends managers ought to discuss preferences for technology use in the workplace and employees may benefit from increased transparency about technology usage. Of course, laptops, cell phones, and other ICTs can be both distracting and beneficial to meeting participants.

Reinsch et al. (2008) contend multicommunication is a structuring process: multicommunication is understood as both the process and outcome, shaping and being shaped by organizational expectations and norms. For example, the decision to engage in multicommunication is based on perceived organizational norms and accounted for 40% of variance in the decision of working adults to engage in multicommunication during meetings (Stephens & Davis, 2009). A person’s expectations about the acceptability of multicommunication guides both their willingness to engage in the behavior and their evaluation of others who do. Managers give meaning to the multicommunication by (not) communicating their view on using ICTs during meetings (Stephens, 2018). Therefore, we propose that when managers articulate a policy about technology use:

H1: Manager policy articulation in favor of technology use (relative to no policy or a policy against technology use) leads to (a) lower expectancy violations, (b) higher communicator evaluations, (c) higher competence ratings, and (d) higher ratings of meeting effectiveness when a meeting member engages in multicommunication.

Breaking norms

Overall, technology use in meetings is nearly ubiquitous (Anderson & Raine, 2018), but there are also strong normative forces counteracting this behavior (see Reinsch & Turner, 2019). Organizational norms promote “continual connectivity, vigilant availability, and responsiveness” (Mazmanian et al., 2013, p. 1350). Yet, engaging in multiple simultaneous conversations via ICTs often results in more errors and reduced accuracy (process loss; Cameron et al., 2018). Though this tension is obvious to organizational members, many still multicommunicate, prompting researchers to ask: “Are the thousands of frequent (even avid) multicommunicators blind to the effect of their own behavior?” (Reinsch & Turner, 2019, p. 164). In short, multicommunication can be normative or counter-normative, depending on the situation.

Evidence suggests task-related multicommunication generates positive evaluations (Cameron et al., 2018; Dennis et al., 2010; De Bruin & Barber, 2019). EVT predicts the valence of the expectancy violation ought to drive assessments of the communication outcomes (Burgoon & Hale, 1988). This may explain why on-task multicommunication behaviors are perceived as more engaged, compassionate, and less rude (De Bruin & Barber, 2019). When it is clear that multicommunication supplements conversation, the act is welcome (Reinsch et al., 2008).

However, evidence suggests that perceptions of multicommunication are introspectively biased: while people tend to think of their own use of phones during meetings as productive, they see others’ use of devices as disruptive (Böhmer et al., 2013). This introspective bias might be alleviated when others justify their actions (e.g., “I’m sorry, I was just IMing with marketing about this project.”). Simply put, when individuals’ motives are more transparent, their multicommunication behavior ought to be less of an expectancy violation. Thus, we predict that multicommunicators who acknowledge their secondary conversation are task-related will be evaluated more positively than those who do not (Burgoon & Hale, 1988; Cameron & Webster, 2011). Formally we hypothesize:

H2: Multicommunicators who acknowledge their secondary conversation is task-related will generate (a) lower expectancy violations, (b) higher communicator evaluations, (c) higher competence ratings, and (d) higher ratings of meeting effectiveness.

Mobile phone-use

Stephens and Davis (2009, p.66) point out that “portability, device size, and integration” of devices all relate to the likelihood that an ICT will be used to multicommunicate in meetings; however, ICT use may be associated with detrimental interpersonal and organizational effects. Evidence suggest mobile phubbing (snubbing someone in favor of a mobile device) promotes conflict and reduces relational satisfaction among romantic partners (Roberts & David, 2016), and violates expectations in family contexts, reducing well-being in older adults (Kadylak, 2020). Further, the use of cell phones is perceived as more inappropriate than the use of a laptop in meetings (Bajko, 2012). Phone use in meetings also reduces attention, creates more errors, delays responses, and prompts confusion in meetings and secondary conversations (Cameron et al., 2018). The pattern is clear, phone use in social situations is an expectancy violation and is associated with negative outcomes.

When asked about the appropriateness of technology use during meetings, three-in-four participants reported laptop use was acceptable, whereas only one-in-four approved of cell phone use (Bajko, 2012). Though cell phone use is ubiquitous, most professionals still consider mobile device usage during meetings inappropriate (Washington et al., 2014). While discrete, the mobile phone tends to be seen as less acceptable than a laptop device. Given this evidence, we predict that the use of a cell phone will constitute a greater expectancy violation than the use of a laptop or notepad. Formally, we hypothesize:

H3: Multicommunicators who use a cell phone to multicommunicate during a meeting generate (a) higher expectancy violations, (b) lower communicator evaluations, (c) lower competence ratings, and (d) lower ratings of meeting effectiveness.

Interaction between manager expectations and employee behavior

Finally, we propose that manager’s policy and employee’s task-acknowledgment interact. Logically, if a manager encourages technology-use during meetings and an employee uses technology to supplement the topic of the meeting, that employee ought to be perceived positively (Stephens, 2018). Restated, when a manager’s expectation that technology use is (un)acceptable matches an employee’s use of technology to benefit the group in a meeting, that employee is not likely to violate expectations, and ought to be positively evaluated for following their manager’s instructions. Thus, we propose:

H4: A match between manager policy and employee-use of technology will (a) not be significantly related to expectancy violation and will generate (b) a higher communicator evaluation, (c) higher competence ratings, and (d) higher ratings of meeting effectiveness.

Method

Participants

Participants were recruited from Amazon Mechanical Turk (MTurk) in return for US$1.30. We followed MTurk best practices: using the platform ourselves, setting reasonable pay rates, setting a threshold for completed tasks (>500), soft-launching the survey, and including attention- and manipulation-check questions (Rouse, 2015; Sheehan, 2018). A total of 363 participants completed the experiment.

Attention and manipulation checks. We used a battery of objective questions to ensure the participants saw the manipulations and were paying attention. Participants who did not identify the manager’s picture (n = 11), notice the actors using a cell phone, laptop, or notepad (checklist, n = 51), notice the manager’s policy about technology use (n = 42), or take note of the apology (n = 8) were excluded, sequentially, from analysis.¹ Finally, those who missed more than half of the directed response attention checks (i.e., “To show you are attentive, select agree,” n = 4) and participants missing data for any outcome variable (n = 4) were also excluded. Thus, 243 participants were included in the analyses reported here. A priori power analyses suggest sufficient power (η² = .10; β > .80, Cohen, 1992).

Demographics. In the final sample 150 participants were male (61.7%), 204 were White (84.0%), and 172 (70.8%) had an associate-level degree or higher. Most participants engaged in one to five meetings per week (n = 219, 90.1%) while the remainder reported having more than five meetings. Most participants worked 31–40 hours per week (n = 151, 62.1%) while the remainder worked more. Participants identified as frontline employees (n = 149, 61.3%), mid-level managers (n = 80, 32.9%), and the rest as management. Participants primarily worked at companies with more than 100 employees (n = 138, 56.8%). Eighty participants (32.4%) earned less than US$40,000, 106 (43.6%) earned between US$40,000 and US$80,000, and the remainder earned more or did not report. Due to a survey error, we did not collect participant’s age; the average age of US Mechanical Turk Workers is 31.6 years (Levay et al., 2016).

Experimental procedures. Participants were exposed to two videos, sequentially, which demonstrated the manipulations of a manager articulating a technology policy (Vignette 1, see Appendix) during an interview and a meeting in which an actor engaged in multicommunication or took notes on a notepad (Vignette 2, see Appendix). Specifically, participants were told: “The following pages contain an interview with an HR manager at a company called Bright Brands and a brief meeting held in Fall 2018. Please watch the videos carefully so you can answer questions about them afterwards.” The first video showed the manager policy manipulation (three manager policy vignettes: none, pro-technology, anti-technology), this video only manipulated the policy. The second video included device manipulations (device: notepad, laptop, phone) and task-acknowledgment (no task-acknowledgment, apology for working on-task), creating six multicommunication vignettes. Two separate videos were used to increase experimental control by: (a) isolating major manipulations into shorter clips and (b) reducing the number of recorded vignettes. The manager in the first vignette leads the meeting in the second vignette.

Participants were shown a picture of the multicommunicating actor and told that they had been randomly assigned to answer questions about that person, a female character named Pat. The other meeting participants were 25–35 years old with a mixture of males and females. The manager, played by the first author, was a male (see Figure 1). We chose not to manipulate communicator gender, based on recent evidence that communicator gender has no effect on multicommunicator perceptions; however, we do include participant gender as a covariate (Paskewitz & Beck, 2019). The order of manipulations was consistent: manager policy, device use, then task-acknowledgment. The videos were filmed to be nearly identical in length and content, with the exception of the manipulations. Participants were required to watch the entire video before they could advance the survey. The full scripts are in the online Appendix with manipulations in brackets.

Figure 1.

Screenshot of meeting showing Pat using the computer.

Measures

All measures were based on pre-existing scales. Unless otherwise noted, scales were measured on a 1 to 7 Likert-type scale where 1 = strongly disagree, 7 = strongly agree. Table 1 presents correlations among dependent variables and covariates. Means by condition are presented in Table 3 and Table 4. Instructions and measure targets were adapted to suit the meeting context.

Table 1.

Correlations among Key Study Variables.

	Range	M	SD	1	2	3	4	5	6	7	8	9	10	11
1. Meetings per week	1–4	3.30	1.23	–
2. Employees at company	1–9	5.92	2.42	0.12	–
3. Management level	1–3	1.45	0.61	0.09	−0.13^*	–
4. Biological sex (2 = Female)	1–2	1.38	0.49	–0.01	0.07	0.04	–
5. Income	1–12	6.27	2.90	0.20^***	0.20^**	0.10	0.05	–
6. Education	1–8	4.31	1.21	0.15^*	0.08	0.13^*	0.11	0.22^***	–
7. Preference for polychronicity	1–7	2.82	1.25	0.11	−0.04	0.04	−0.02	0.08	0.07	–
8. Multicommunication norms	1–7	4.42	1.47	0.18^**	0.19^**	−0.01	−0.03	0.11	0.20^***	0.17^**	–
9. Expectancy violations	1–7	4.14	1.59	−0.17^**	−0.11	0.05	0.10	0.04	–0.06	−0.20^***	−0.31^***	–
10. Communication evaluation	1–7	3.85	1.42	0.08	0.09	−0.05	−0.14^*	−0.11	0.04	0.28^**	0.20^**	−0.80^***	–
11. Communicator competence	1–7	4.43	1.24	0.04	0.11	−0.01	−0.06	0.04	0.03	0.23^**	0.13^*	−0.68^***	0.72^***	–

Correlation significant at: ***p < .001 level, **p < .01 level, *p < .05 level; ² = Squared values; details presented in Measures section

Table 2.

MANCOVA Results.

IV	DV	Df	Univariate F	η²_partial	Df	Stepdown F
Manager policy	Expectancy violation	2, 227	6.05**	0.05	2, 227	3.95*
	Communicator evaluation	2, 227	5.46**	0.05	2, 226	1.05
	Perceived competence	2, 227	8.38***	0.07	2, 225	2.14
	Meeting effectiveness	2, 227	5.91**	0.05	2, 224	2.78
Task-acknowledgment	Expectancy violation	1, 227	1.46	0.01	1, 227	1.30
	Communicator evaluation	1, 227	0.02	0.00	1, 226	1.50
	Perceived competence	1, 227	0.00	0.00	1, 225	0.67
	Meeting effectiveness	1, 227	0.49	0.00	1, 224	0.71
Device	Expectancy violation	2, 227	59.61***	0.34	2, 227	59.91***
	Communicator evaluation	2, 227	36.64***	0.24	2, 226	1.73
	Perceived competence	2, 227	29.36***	0.21	2, 225	0.36
	Meeting effectiveness	2, 227	7.50***	0.06	2, 224	0.21
Manager policy × Task-acknowledgment	Expectancy violation	2, 227	0.43	0.00	2, 227	0.43
Manager policy × Task-acknowledgment	Communicator evaluation	2, 227	2.70	0.02	2, 226	3.35*
	Perceived competence	2, 227	5.34**	0.05	2, 225	4.11*
	Meeting effectiveness	2, 227	0.51	0.00	2, 224	2.22

***p < .001, **p < .01, *p < .05

Note: The Roy-Bargman stepdown approach (via SPSS 25 MANOVA function) isolates effects for DVs by sequentially covarying out prior DVs. We include this test to demonstrate that expectancy violation is the mechanism explaining outcome evaluations.

Table 3.

Means and Standard Deviations by Manager Policy and Device.

	No policy			Anti-Technology Policy			Pro-Technology Policy
	Notepad(n = 23)	Laptop(n = 24)	Cell phone(n = 25)	Notepad(n = 35)	Laptop(n = 29)	Cell phone(n = 25)	Notepad(n = 25)	Laptop(n = 24)	Cell phone(n = 33)
Expectancy violation	3.21 (1.24)	4.55 (1.67)	5.25 (1.17)	2.92 (1.40)	4.62 (1.39)	5.43 (0.96)	2.72 (1.23)	3.69 (1.32)	4.81 (1.10)
Communicator evaluation	4.30 (1.61)	3.58 (1.50)	2.77 (1.00)	4.88 (1.38)	3.59 (1.04)	2.67 (0.80)	4.90 (1.24)	4.38 (1.21)	3.45 (0.99)
Perceived competence	4.56 (0.94)	4.06 (1.33)	3.81 (1.20)	5.22 (1.15)	4.09 (1.12)	3.44 (1.14)	5.48 (0.94)	4.92 (1.05)	4.14 (0.86)
Meeting effectiveness*	24.64 (10.12)	24.91 (8.97)	23.21 (11.51)	32.47 (9.83)	26.29 (10.21)	24.83 (9.33)	33.25 (9.68)	32.18 (7.69)	24.91 (10.79)

Note: All 7-point scales, the meeting effectiveness value is squared to yield a more normal distribution.

Dependent measures

Expectancy violations. Expectancy violations were measured using Burgoon and Walther’s (1990) scale of expectedness. Expectedness is a four-item scale measuring the extent to which behavior exhibited by the target conforms with normative behavior. Items included: “Pat’s behavior is appropriate” and “Pat’s behavior is how you would expect most people to behave.” The scale demonstrated adequate reliability (α = .89).

Communicator evaluation. We also used Burgoon and Walther’s (1990) scale of communicator evaluation. Items included: “Most people would like to interact with Pat,” “Pat’s behavior is likely to please meeting members,” and “Pat’s behavior is undesirable.” The scale was reliable (α = .91).

Perceived competence. Competence was measured using McCroskey and Tevan’s (1999) communication competence scale. This scale includes six 7-point bipolar semantic differential rating using the recommended instructions. The scale was reliable (α = .94) and item anchors were: intelligent/unintelligent, trained/untrained, inexpert/expert, informed/uninformed, incompetent/competent, and bright/stupid.

Meeting effectiveness. We use a modified version of Burgoon and Hale’s (1988) communicator effectiveness scale to measure meeting effectiveness. Following Cameron et al. (2018) we used five-items, including: “The meeting was efficient” and “The meeting was successful.” The scale was acceptably reliable (α = .91).

Covariates

Preference for polychronicity. Polychronicity is a proclivity to engage in multiple conversations at once. Turner and Reinsch’s (2007) polychronic communication orientation scale was used to capture personal preference for polychronicity. Items include “I like to manage multiple conversations at the same time” and “People should try to manage multiple communication tools at once.” The scale was reliable (α = .88).

Multicommunication preference. Stephens and Davis’ (2009) workplace norms for multicommunication scale was used to capture participants’ workplace practices. Items include: “It is rare to attend a meeting where people are not using some form of communication technology” and “I often see (or hear) others using technology during work meetings.” The scale was reliable (α = 0.86).

Results

In line with the hypotheses, we conducted a three (manager policy: none, pro-technology, anti-technology) by three (device use: notepad, phone, laptop) by two (no task-acknowledgment versus task-acknowledgment) multivariate analysis of covariance (MANCOVA) using the SPSS 25 GLM procedure. The relevant dependent variables were as follows: expectancy violation, communicator evaluation, perceived competence, and meeting effectiveness. Results of the assumptions of normality, homogeneity of variance, and multicollinearity/singularity were tested using recommendations by Tabachnik and Fidell (2013). The meeting effectiveness scale was negatively skewed and leptokurtotic; however, the distribution was made normal by taking squared values of the variable (Fink, 2009). Levene’s test was significant for communicator evaluation; however, the ratio of largest to smallest variances did not exceed the 7:1 so we proceeded with the analysis (Tabachnik & Fidell, 2013). There were no problematic univariate or multivariate outliers. Results for the full MANCOVA are show in Table 2.

Because one’s perceptions of normative behavior and workplace experiences affect perceptions of multicommunication, we included several covariates in the MANCOVA. Specifically, we included the average number of meetings per week, company size, managerial status, biological sex, income, education, as well as normative scales of preference for polychronicity, and perceived workplace multicommunication norms (Paskewitz & Beck, 2019; Turner & Reinsch, 2007). Finally, we computed a step-down version of the MANCOVA (rightmost column, Table 2) for the interaction hypotheses (H4a–H4d); this model covaries out dependent variables (DVs) in order of entry. The Roy-Bargman stepdown model allows us to isolate the relative importance of expectancy violations and subsequent evaluations in the model (see Discussion).

Covariates

The MANCOVA showed the combination of DVs was significantly predicted by the covariates, F(32, 828) = 2.60, Wilks’ λ = 0.70, p < .001, η²_partial = 0.09. In the univariate decomposition, three covariates were significant. Expectancy violations were significantly affected by multicommunication norms (B = −0.29, t = −5.17, p < .001, η²_partial = 0.11). The more normative multicommunication was in one’s workplace, the less of an expectancy violation. Communicator evaluation was significantly related to income (B = −0.64, t = −2.32, p = .021, η²_partial = 0.02), multicommunication norms, (B = 0.14, t = 2.59, p = .010, η²_partial = 0.03), and preference for polychronicity (B = 0.21, t = 3.18, p = .002, η²_partial = 0.04). Those with higher income evaluated the communicator more negatively; whereas, those whose workplace had multicommunication norms and who engaged in polychronicity practices rated the multicommunicator higher. No covariates were significantly related to perceived competence or meeting effectiveness. Covariate-adjusted results are presented below.

Manager policy

H1 proposed manager policy articulation would significantly affect expectancy violation, communicator evaluation, perceived competence, and meeting evaluation. Manager policy was significant, including covariates: F(8, 448) = 3.16, Wilks’ λ = 0.90, p = .002, η²_partial = 0.05. Univariate analysis revealed significant effects for expectancy violations: F(2, 242) = 6.05, p = .003, η²_partial = 0.05, supporting H1a; communicator evaluation, F(2, 227) = 5.46, p = .005, η²_partial = 0.05, supporting H1b; competence, F(2, 227) = 8.38, p < .001, η²_partial = 0.07, supporting H1c; and perceived meeting effectiveness, F(2, 227) = 5.91, p = .003, η²_partial = 0.05, supporting H1d.

Means across these conditions are presented in Table 3. Post hoc contrasts comparing the manager policy conditions to the mean reveal that the manager policy in favor of technology usage uniformly generated lower expectancy violations (p = .001), higher communicator evaluations (p = .001), competence ratings (p = .001), and meeting effectiveness ratings (p = .012). However, the anti-technology policy was not significantly different from scale means. Thus, as hypothesized, the pro-technology policy is the important source of variance in expectancy violations. H1a through H1d were all supported.

Task-relatedness

H2 proposed that acknowledging one’s multicommunication as task-related would reduce expectancy violations and increase evaluations of the communicator, the communicator’s competence, and the meeting effectiveness. Against our hypothesis, task-acknowledgment was not multivariate significant: F(4, 224) = 0.83, Wilks’ λ = 0.99, p = .51, η²_partial = 0.02. Thus, H2a – H2d were not supported. There was no significant effect for task-acknowledgment.

Device

H3 predicted that there would be a significant main effect for device such that the use of a cell phone would prompt higher expectancy violations along with lower communicator evaluation, competence, and meeting effectiveness ratings. After controlling for covariates, device generated a significant main effect: F(8, 448) = 13.91, Wilks’ λ = 0.64, p < .001, η²_partial = 0.20. Univariate tests reveal that device affected ratings of expectancy violations, F(2, 227) = 59.61, p < .001, η²_partial = 0.34, supporting H3a; communicator evaluation, F(2, 227) = 36.64, p < .001, η²_partial = 0.24, supporting H3b; competence ratings, F(2, 227) = 29.36, p < .001, η²_partial = 0.21, supporting H3c; and meeting effectiveness, F(2, 227) = 7.50, p = .001, η²_partial = 0.06, supporting H3d.

The means for device ratings are shown in Table 3. Post hoc contrasts comparing devices to the mean show that the notebook generated a significantly smaller expectancy violation (p < .001) and higher communicator evaluations (p < .001), competence ratings (p < .001), and meeting effectiveness ratings (p = .002). Contrasting laptop computers against the mean showed ratings were not significantly different from the mean across dependent variables. As shown in the means, phone use solicited the highest expectancy violation and the lowest outcome evaluations (all p < .001). Overall, this test supports H3a–H3d.

Interaction between manager policy and task-acknowledgment

Finally, H4a–H4d predicted an interaction between manager policy and task-acknowledgment such that policy match with technology use in task-related ways would yield the highest communicator evaluations, competence, and meeting effectiveness ratings. Because this hypothesis seeks to isolate the effects of expectancy violation (i.e., H4a) and the outcome measures, the Roy-Bargman stepdown approach was used to enter the DVs sequentially (see rightmost columns in Table 2; Tabachnik & Fidell, 2013). Manager policy by task-acknowledgment yielded a significant multivariate effect, controlling for covariates: F(8, 448) = 2.53, Wilks’ λ = 0.92, p = .011, η²_partial = 0.04. H4a predicted expectancy violation would not vary based on the policy and task-acknowledgment interaction and this effect was not significant (p = .654). H4b through H4d predicted policy and task-acknowledgment would interact affecting: communicator evaluation, F(2, 226) = 3.35, p = .037, η²_partial = 0.03; competence ratings, F(2, 225) = 4.11, p = .018, η²_partial = 0.04; and, meeting effectiveness (p = .111). Overall, H5a, H5b, and H5c were supported, while H5d was not.

Table 4 shows means and standard deviations for manager policy by task-acknowledgment. When the manager articulated a policy, those who acknowledged their multicommunication were evaluated higher and seen as more competent. In the absence of a policy, the pattern is reversed. Finally, the means for communicator evaluation and competence were highest in the pro-technology policy condition. In all, when the manager’s policy is matched by employee’s behavior outcome means tend to be higher.

Table 4.

Means and Standard Deviations by Manager Policy and Task-Acknowledgment.

	No policy		Anti-Technology Policy		Pro-Technology Policy
	No acknowledgment(n = 37)	Acknowledge(n = 35)	No acknowledgment(n = 41)	Acknowledge(n = 48)	No acknowledgment(n = 43)	Acknowledge(n = 39)
Expectancy violation	4.05 (1.61)	4.20 (1.49)	4.20 (1.52)	4.16 (1.78)	3.71 (1.60)	3.99 (1.34)
Communicator evaluation	3.73 (1.50)	3.32 (1.51)	3.72 (1.59)	3.94 (1.33)	4.06 (1.33)	4.27 (1.23)
Perceived competence	4.38 (1.08)	3.86 (1.27)	4.22 (1.42)	4.46 (1.29)	4.60 (1.00)	4.97 (1.17)
Meeting effectiveness*	24.30 (10.30)	24.17 (10.15)	29.82 (7.51)	27.02 (12.11)	29.34 (10.16)	29.85 (10.53)

Note: All 7-point scales, the meeting effectiveness value is squared to yield a more normal distribution.

Discussion

EVT is a powerful framework for understanding how multicommunication behavior is perceived. Our results reveal the importance of manager policy and device use in multicommunication perceptions. Against a robust set of findings (Cameron et al., 2018; De Bruin & Barber, 2019; Reinsch et al., 2008), task-acknowledgment was unrelated to expectancy violations, communicator evaluation, competence, or meeting evaluations. It appears that the act of multicommunicating supersedes the subsequent explanations of behavior, which informs EVT and mobile media theorizing. One exception is the match between manager policy and meeting behavior improved evaluations. This discussion presents theoretical and applied implications focusing on a mobile introspective illusion, the importance of manager policy-sharing, and the importance of expectations.

Though meetings require less focused attention than dyadic conversation (Paskewitz & Beck, 2019), engaging with mobile devices during meetings generates attributions by communication partners. These attributions are partially driven by the perceived capabilities of technology and materiality (i.e., physical presence) of the devices (Mannell, 2019). In other words, there is a tension between a phone’s capabilities (e.g., facilitating communication with experts) and the perceived use of devices (e.g., used for distraction rather than on-task activities). In this way, the materiality of a phone, laptop, or notepad evokes perceptions of the person, the context, and the opportunities for action (Mannell, 2019). Device perceptions are tied to expectancy violations (Burgoon, 1993).

Mobile introspective illusions

Meaning inference for mobile use differs between users and perceivers (Reinsch & Turner, 2019) like many self-other perceptual processes (Pronin et al., 2004). When comparing their own mobile behavior relative to norms, people estimate their own behavior is more normative than a partner’s (Hall et al., 2014). This is an introspection illusion whereby our own thoughts and motives are easy to interrogate and understand, but the thoughts and motives of others are at odds with our own cognitions (see Pronin et al., 2004). Multicommunication practices in one’s own workplace and preferences for engaging in multiple conversations at once both increased communicator evaluations—but neither eliminated the effects of perceiving a meeting member engage in multicommunication. There seems to be a tension between one’s own use of a device (which meeting participants believe improves decision outcomes and personal performance; Dennis et al., 2010) and the use of a device by others (which meeting participants evaluate negatively; Cameron & Webster, 2011; De Bruin & Barber, 2019). While phones are perceived to generally improve work outcomes (Smith & Anderson, 2017) this belief is biased toward our own work, not the work others are doing.

Mobile-material assumptions. The self-other discrepancies in device perception between this study and past studies suggests that newcomers ought to assume others will infer the worst when they use devices. Mobile devices prompt material-driven assumptions: mobile devices are assumed to distract. Though portable, mobile devices facilitate (distracted) communication with organizational insiders and outsider parties (Mannell, 2019; Schrock, 2015); concurrently, people seem to tolerate their own use of mobile phones, but are less accepting of others engaging in the ‘distracting’ behavior (see also, Böhmer et al., 2013). In this way, the assumptions evoked by the material presence of the devices supersedes the realistic capabilities of the device. Phone use generates very high expectancy violations, which explain 20% of variance in evaluations. Whether joining a new organization, engaging in cross-functional teams, or going to a meeting outside of one’s typical team, taking notes on paper matches expectations, while using a laptop or, especially, a phone does not. For an onlooker who does not know what is happening on another’s screen, any use of mobile media violates expectations and invites negative evaluations (see, Böhmer et al., 2013).

The strength of articulating technology policies

Our findings validate Stephens’ (2018) contention that managers clearly communicating technology use expectations has meaningful implications for how technology use is perceived. Managers can structure technology use norms for meetings, and likely other work domains, in ways that affect communicator evaluations (Reinsch et al., 2008). Thus, well-articulated policies may represent a powerful intervention in making multicommunication (un)acceptable in meetings and ought to be leveraged by researchers and practitioners alike.

The weakness of task-acknowledgment. A wide variety of research suggests that on-topic multicommunication ought to be evaluated positively as complimentary functions should aid conversation (Cameron et al., 2018; De Bruin & Barber, 2019). To our surprise, task-acknowledgment alone had no bearing on expectancy violations. This could be because the verbal message holds less value than the nonverbal action of using a device during the meeting (Hale & Stiff, 1990). When De Bruin and Barber (2019) directly revealed motives to participants, the multicommunicator was evaluated more positively. In contrast, our null findings prompt as many questions as they pose answers: Is it possible to excuse one’s distracted behavior? Under what conditions does multicommunication become normative? How can technology users communicate their task-related actions without violating expectations?

Matching policy and task-acknowledgment. When task-acknowledgment was combined with manager policy, task-acknowledgment did affect outcome evaluations. A technology supportive policy articulation combined with task-acknowledgment prompted the highest outcome ratings (i.e., communicator and competence; see Table 4). The combinatorial effect of manager messages and member acknowledgment of task-based work suggests that alignment of expectations may buffer negative impressions of multicommunication behavior. Again, this highlights the importance of social norms in dictating how multicommunication behavior is interpreted (Stephens & Davis, 2009; Stephens, 2018).

Only the technology-embracing policy differed significantly from the mean. This may signal that in the absence of a policy, workers assume that technology ought not be used during meetings. However, we also know that creating workplace structures, like culture and norms, is difficult work. In aggregate, a reinforced technology policy and peer expectations are important tools that leaders can use to change behaviors regarding technology usage (Stephens & Davis, 2009). Managers should take time to set (and model) clear policies for technology use (Stephens, 2018). Further, EVT is prescriptive for interacting in unfamiliar meeting environments (and likely other workplace environments): technology use, even when it is related to the task, should be assumed to counter norms until other evidence (e.g., manager policy, peer use) suggests otherwise.

EVT and multicommunication

The final contribution is the utility of EVT as a framework to integrate disparate multicommunication findings. Specifically, past studies have used social exchange theory, incivility (Cameron & Webster, 2011), politeness theory (Cameron et al., 2018), media richness theory (Turner & Reinsch, 2007), and the social influence perspective (Stephens & Davis, 2009). We leverage these findings and fit them concisely in the EVT framework. Thus, EVT proved a particularly pointed explanation for participants’ assessments of the multicommunicator, media use, and the meeting. As shown in the rightmost columns of Table 2, using the stepdown approach to assess the DVs, changes in these outcome evaluations were almost entirely driven by expectancy violations.

This study tests how a meeting outsider might perceive multicommunication in a meeting. In other words, our experiment is akin to seeing a new person meeting with a group, in or out of one’s regular workplace, and seeing attendees (not) engage in multicommunication. Results show that an onlooker can quickly catch on to norms (e.g., manager policies), have their expectations violated, form impressions of a communicator who engages in multicommunication and assess meeting effectiveness. Picking up or putting down a device during a meeting is a powerful cue in work and interpersonal relationships (Hall et al., 2014).

Order effects in EVT. It may be quite difficult to un-violate expectancies. The order of our experimental manipulations showed the multicommunication first, then device use, then the actor acknowledged the task. Perhaps if the actor had said “I’m going to message someone who knows about this project,” before using the device, the expectancy violation would not have occurred. Certainly, this is an empirical question: Does the order of task-acknowledgment mitigate expectancy violations associated with multicommunication and mobile media use? When we designed the experiment, we took for granted that including all three manipulations would imply an order effect (i.e., manager policy, multicommunication, then task-acknowledgment). Had we mixed the order of task-acknowledgment and multicommunication it is possible that task-acknowledgment would have yielded a significant effect. Similarly, we might ask, if the manager’s policy were made clear after-the-fact would the effect still be meaningful? Future research should test how forewarning technology use relates to expectancy violations.

Limitations and future directions

This sample is limited: it is more white, educated, and male than the average US worker. Further, age was not collected in the survey. Future research might conduct stratified sampling to see how race, gender, age, and other demographic attributes relate to the perceptions of technology use in meetings. Future research can use EVT to refine how communicator reward (e.g., homophilous communicators) and order of events might affect perceptions of mobile users.

This experiment focuses on a zero-history meeting context. While this affords control, it cannot account for multicommunication in established teams. Future work might explore how team-norms and individual personalities interact to explain multicommunication in more complex contexts. Additionally, both the quality and amount of communication between the multicommunicator and the team in the meeting was low (one line in each condition). Teams with more established relationships and richer communication might see differing outcomes than those found here. Both polychronicity and multicommunication norms related to outcome evaluations; logically, there is a relationship between one’s workplace norms, established relationships, and perceptions of multicommunication behaviors. In the EVT framework additional expectancies based on relationship and context require researchers’ attention (Burgoon, 1993).

One important relational structure theorized to determine the acceptability of ICT use during meetings is power and status (Reinsch & Turner, 2019). Given the added design complexity, this study was unable to manipulate the status of the multicommunicator. Future research should explore how power dynamics affect perceptions of multicommunication, in context. For example, how does the multicommunication of a manager or leader differ from that of an administrative assistant or front-line worker?

Though our experiment gives us control over the causal mechanism under investigation, it is also limited by our ability to call participants’ attention to manipulations. Though we included attention and manipulation checks, we could certainly have had much more robust and overt manipulations. Instead, we opted for a study with higher ecological validity. We suspect our findings represent a realistic encounter in a new work context, but they are still based on actors seen in a video. Though this study challenges the retrospective approach that has dominated multicommunication research (cf., De Bruin & Barber, 2019; Paskewitz & Beck, 2019) it is still less robust than situated interaction in an established work environment. Future research may benefit from using EVT as a framework to explain multicommunication behavior, in situ. EVT is especially useful as the variance in outcomes was contingent on expectations.

Conclusion

This experiment used video vignettes to test how multicommunication was perceived under three varying conditions: manager technology policy (none, pro-technology, anti-technology), device use (notepad, laptop, cell phone), and task-acknowledgment (no task-acknowledgment, task-acknowledgment). Results showed that manager policy and device both served as informative cues for the perception of multicommunication in meeting contexts. There was not an effect for task-acknowledgment, but acknowledgment did interact with manger policy such that policy alignment with employee behavior during the meeting generated the highest outcome ratings. These results reveal the importance of managers setting expectations for device usage and multicommunication behaviors in organizational contexts. This experiment ties mobile materiality (Hall et al., 2014; Mannell, 2019) and device perceptions to classic literature about expectations (Burgoon & Hale, 1988). The behavior of the participant, rather than the verbal acknowledgment of task-relatedness, drove expectancy violations and outcome evaluations. Results also show general negative evaluations of technology use during meetings, especially the use of mobile phones. Certainly, additional research tying expectations, media, and materiality are warranted. EVT may be useful for understanding mobile media expectations and this study suggest that in the absences of a pre-existing relationship, new meeting members would be best served by taking social cues or avoiding technology use altogether.

Supplemental Material

sj-pdf-1-mmc-10.1177_2050157920927049 - Supplemental material for Expectations of technology use during meetings: An experimental test of manager policy, device use, and task-acknowledgment

Supplemental material, sj-pdf-1-mmc-10.1177_2050157920927049 for Expectations of technology use during meetings: An experimental test of manager policy, device use, and task-acknowledgment by Cameron W. Piercy Greta R. Underhill in Mobile Media & Communication

Footnotes

Acknowledgements

The authors wish to thank Drs. Norah Dunbar, Joann Keyton, and the two anonymous reviewers for valuable feedback on this manuscript. Thanks also to Drs. Michael Ault and Kathryn Lookadoo for assisting with the manipulation vignettes.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This manuscript was supported by the University of Kansas College of Liberal Arts and Sciences Research Mini Retreat Grant and start-up funding.

ORCID iD

Cameron W. Piercy

Author biographies

Cameron W. Piercy, PhD, is an assistant professor Communication Studies at the University of Kansas. His research explores how technology is (re)shaping social networks. He is especially interested in how perceptions of technology affect the way people engage with both technologies and other people. His research has been published in Communication Research, Computers in Human Behavior, and New Media & Society, among others.

Greta R. Underhill, MS, is a doctoral candidate in Communication Studies at the University of Kansas. Her research explores organizational communication, health communication, and implications for people on the autism spectrum. Her publications include a forthcoming case study about online disclosures about work, and she has been selected to participate in the 2020 NCA Doctoral Honors Seminar.

Note

Supplemental material

Supplemental material for this article is available online.

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