Future research directions within themes
| Theme | Future research directions |
|---|---|
| Improve comparability among PPM studies | Use dissatisfaction, alternative attractiveness, and switching costs as baseline independent variables to predict switching intention (and switching behavior) |
| Use the most cited, valid measurement scales provided in this study (see Table 3) | |
| When measuring other independent variables, provide the exact measurement scales that were used | |
| In addition, reporting should include a correlation matrix, a complete description of the sample, the sources and the reliability coefficients of the measurement scales | |
| Expansion of PPM variables | Explore additional independent variables to determine their ability to explain more variance |
| The inclusion of affective variables (e.g. anxiety, regret, affective commitment) could be particularly interesting, as dissatisfaction, alternative attractiveness, and switching costs are cognitive variables | |
| Moreover, the inclusion of context-specific variables (e.g. digital literacy in digital switching contexts) could be interesting and help to explain additional variance | |
| When exploring additional independent variables, be sure to provide a sound theoretical argumentation of whether the variables are push, pull, or mooring variables | |
| Explore moderators | Table 4 offers meta-analytical insights into the effects of study level moderators, providing explanations for differences in effect sizes. To further advance theory development, primary studies should investigate the effects of individual level moderators |
| Examine the role of mooring factors in moderating the relationships between push and pull factors and switching | |
| More studies on actual switching behavior | Conduct studies on actual switching behavior, as there is currently a lack of such studies, to improve practical relevance |
| In doing so, investigate variables that influence the intention-behavior gap (e.g. habit) to better understand why switching intention does not always result in actual switching behavior | |
| When available, use objective data sources (e.g. transaction records, customer logs) to validate self-reported measures of switching behavior | |
| More studies in underexplored or new research contexts | Conduct more studies in underexplored or new consumer service switching research contexts (see Chapter 5.1) to enrich the PPM literature |
| Explore B2B switching contexts, where push, pull, and mooring factors, as well as their influence on switching, may differ significantly from those in consumer service switching |
| Theme | Future research directions |
|---|---|
| Improve comparability among PPM studies | Use dissatisfaction, alternative attractiveness, and switching costs as baseline independent variables to predict switching intention (and switching behavior) |
| Use the most cited, valid measurement scales provided in this study (see | |
| When measuring other independent variables, provide the exact measurement scales that were used | |
| In addition, reporting should include a correlation matrix, a complete description of the sample, the sources and the reliability coefficients of the measurement scales | |
| Expansion of PPM variables | Explore additional independent variables to determine their ability to explain more variance |
| The inclusion of affective variables (e.g. anxiety, regret, affective commitment) could be particularly interesting, as dissatisfaction, alternative attractiveness, and switching costs are cognitive variables | |
| Moreover, the inclusion of context-specific variables (e.g. digital literacy in digital switching contexts) could be interesting and help to explain additional variance | |
| When exploring additional independent variables, be sure to provide a sound theoretical argumentation of whether the variables are push, pull, or mooring variables | |
| Explore moderators | |
| Examine the role of mooring factors in moderating the relationships between push and pull factors and switching | |
| More studies on actual switching behavior | Conduct studies on actual switching behavior, as there is currently a lack of such studies, to improve practical relevance |
| In doing so, investigate variables that influence the intention-behavior gap (e.g. habit) to better understand why switching intention does not always result in actual switching behavior | |
| When available, use objective data sources (e.g. transaction records, customer logs) to validate self-reported measures of switching behavior | |
| More studies in underexplored or new research contexts | Conduct more studies in underexplored or new consumer service switching research contexts (see Chapter 5.1) to enrich the PPM literature |
| Explore B2B switching contexts, where push, pull, and mooring factors, as well as their influence on switching, may differ significantly from those in consumer service switching |
Source(s): Author’s own work
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